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This paper presents an overview of the geometric partitioning mode (GPM) algorithm that is a part of the most recent Versatile Video Coding (VVC) standard. The GPM algorithm aims to increase the partitioning precision of moving objects using non-rectangular and asymmetric rectangular partitions on top of the conventional rectangular block partitioning structure of VVC. Novel features of GPM contributing to the increase in coding efficiency and the reduction in encoder and decoder complexity are detailed and analyzed in this paper. Evaluated with VVC test model version 8.0 under the joint video experts team common test conditions, experimental results show that the presented GPM algorithm provides luma Bjøntegaard Delta rate reduction of 0.70% for random access and of 1.55% for low delay with B slices configurations, with roughly 3% to 5% additional encoding time and negligible decoder runtime change. Furthermore, as GPM provides more precise partitions for the boundaries of the moving objects, an improvement of visual quality is seen in GPM coded sequences. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the decoder-side motion vector refinement (DMVR) algorithm in the Versatile Video Coding (VVC) standard. The proposed DMVR algorithm aims to increase the prediction accuracy of the blocks coded in merge mode using the bilateral matching-based refinement method. Compared with previous decoder-side motion vector derivation approaches, the proposed method significantly increases the coding efficiency without signaling additional side information. Furthermore, the hardware implementation considerations of the DMVR design are particularly focused in this study. This paper details and analyzes the novel features of DMVR contributing to the increase in coding efficiency and the reduction in computational complexity and implementation difficulty. Experimental results based on the VVC test model version 8.0 demonstrate that average Bjøntegaard Delta rate savings of 0.80 % and 2.81 % are achieved for the “tool-off” and “tool-on” test configurations, respectively. Moreover, 4 % additional decoding time and negligible additional external memory bandwidth requirements of DMVR based on the common test conditions for VVC are reported. [ABSTRACT FROM AUTHOR]

Flexible block partition and newly adopted intra coding tools bring significant performance improvement for next generation video coding, and meanwhile introduce non-negligible encoding complexity increment. This paper presents a scalable intra coding optimization (SICO) scheme for the third generation of audio video coding standard (AVS3). The complexity distribution and inheriting relationship among different partitioning, as well as intra prediction modes, are systematically analyzed. Subsequently, low-complexity algorithms are proposed, which could early exclude unlikely coding modes safely. In particular, a data-driven binary classifier is elegantly trained for the determination of coding unit partitioning. Moreover, the preliminary coding information can be exhaustively utilized for the mode decision in a low-cost manner. The proposed method provides scalable intra coding optimizing solutions, which is eligible to cater to various application scenarios. Experimental results show that the proposed method could achieve a wide range of encoding complexity reduction from 18% to 76% with moderate compression performance loss. One implementation of the proposed method has been adopted to the AVS3 reference software. [ABSTRACT FROM AUTHOR]

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance. [ABSTRACT FROM AUTHOR]

Dictionary learning has achieved remarkable success on a wide range of machine learning-based applications. In this paper, a locality-adaptive structured dictionary learning (LASDL) algorithm for cross-domain recognition is proposed. In the LASDL, a projective structured double reconstruction strategy is developed to train class-oriented sub-dictionaries from the specific classes of cross-domain samples. The strategy benefits to make full advantage of the discriminative information of cross-domain data and bridge the distribution divergence between two different domains. Meanwhile, an adaptive geometrical structure preserving function is designed to not only preserve the local manifold structures spanned by the representation coefficient spaces of the source and target domains, but also impose a constraint that the coefficients should keep closer to their class centers, which is propitious to reduce the distribution divergence and make the representation more accurate. With the structured linear coding technique, the final cross-domain recognition can be efficiently performed by determining class-specific reconstruction error. The optimization of the proposed LASDL model can be efficiently solved by simple least square method and alternating direction method of multipliers (ADMM) algorithm. Extensive experimental results validate the superiority of the proposed algorithm in contrast to other state-of-the-art predecessors. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as Algorithm Unrolling Image Fusion (AUIF), is proposed to overcome the shortcomings of traditional CNN-based IVIF models. The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i.e., separating low-frequency base information and high-frequency detail information from source images. Then the algorithm unrolling is implemented where each iteration is mapped to a CNN layer and each optimization model is transformed into a trainable neural network. Compared with the general network architectures, the proposed framework combines the model-based prior information and is designed more reasonably. After the unrolling operation, our model contains two decomposers (encoders) and an additional reconstructor (decoder). In the training phase, this network is trained to reconstruct the input image. While in the test phase, the base (or detail) decomposed feature maps of infrared/visible images are merged respectively by an extra fusion layer, and then the decoder outputs the fusion image. Qualitative and quantitative comparisons demonstrate the superiority of our model, which can robustly generate fusion images containing highlight targets and legible details, exceeding the state-of-the-art methods. Furthermore, our network has fewer weights and faster speed. [ABSTRACT FROM AUTHOR]

As video data are occupying an increasingly more significant portion of global data traffic, video communication has become an indispensable component for most multimedia applications. As an enabling technology of video communication, although video coding is well standardized, the strategy to control video codec is highly customized to applications. The consistency of video quality is being paid more attention in many emerging applications. For example, in video surveillance, the quality of video frames should be stable in order to ensure the performance of machine intelligence algorithm, such as object detection precision. In this paper, we focus on achieving constant objective reconstruction quality in the process of video coding. To achieve certain rate-distortion performance, bit rate and distortion metric are usually modeled as a function of video content and control parameters of codec. For content modeling in existing work, there is still room for improvement, including the design of more efficient content feature, the compensation for assumption about constant RD characteristics among consecutive frames, and the adjustment of Lagrangian multiplier $\lambda $ according to content property. The main contributions of this paper are: 1) a robust content adaptive model for residual bit rate modeling based on content statistics called mean absolute partial transformed difference (MAPTD); 2) a content-related header bit rate modeling; 3) preprocessing scheme for robust content feature estimation at scene change; 4) a distortion model consistent with local content; and 5) content adaptive $\lambda $ determination. The experimental results show that our constant quality control strategy can achieve superior performance compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The new generation of the high-efficiency video coding (HEVC) video coding standard has improved compression performance and brought considerable coding complexity. Therefore, reducing the perception redundancy of video to obtain a better compression effect is the new direction of video development at present. In this paper, the HEVC is improved and enhanced from the aspects of a video saliency algorithm based on an attention mechanism and a video compression algorithm based on perception priority. In terms of video saliency, this paper proposes a spatial saliency algorithm based on a convolutional neural network and a temporal saliency algorithm based on motion vector. The saliency algorithm can combine the motion estimation results of each block during the HEVC compression on the basis of convolutional neural network and carry out adaptive dynamic fusion of the two, to complete the saliency map of the input video. In the aspect of the video compression algorithm with perception priority, this paper proposes a more flexible QP selection method, which selects its corresponding QP according to the saliency value of CU. At the same time, we propose a new rate-distortion optimization algorithm, which integrates the current block’s saliency feature into the traditional rate-distortion calculation method, to guide the allocation of bits and achieve the purpose of perception priority. The experimental results proved the superiority of the proposed method over the state-of-the-art perceptual coding algorithms in terms of saliency detection and perceptual compression quality. [ABSTRACT FROM AUTHOR]

Wavelet transform using Haar filter is a fast process for decomposing an image into low and high frequency sub-bands, which is an important step in multi-focus image fusion. This transformation alone is usually not sufficient to produce satisfying fused images, and therefore additional tools like focused region decision map and feature extraction are needed to enhance the fusion quality. In this paper, we propose a multi-focus image fusion algorithm that is suitable for efficient hardware implementation. The algorithm speed is significantly improved by utilizing Haar wavelet and computationally simple fusion rules. To further improve the circuit power and delay, we limit the logic operations to additions, subtractions, and multiplications only. Experiments on different benchmarks demonstrate that our proposed algorithm can reduce the fusion time by up to 99.97% compared with the state-of-the-art competing methods without compromising the fusion quality. In addition, the hardware implementation of the proposed algorithm reduces the circuit area and delay by 79.5% and 92.5%, respectively, when compared to the most competitive algorithm in this paper while maintaining similar fusion quality. [ABSTRACT FROM AUTHOR]

Color demosaicing is an essential step in the camera image processing pipeline, especially on hardware platforms for real-time video applications. Though many demosaicing algorithms have been proposed in the past two decades, there remains a substantial gap between industrial needs and academic research. On one hand, industry requires a high perceptual quality, artifact-free, and low-cost demosaicing algorithm that is noniterative and small window based ready to be implemented on a hardware platform with a limited line buffer. On the other hand, academia is targeting high PSNR/SSIM, with the computation cost and line buffer receiving second priority, and often a frame buffer and iterative operation are used. The cost, large line buffer and iteration requirement make most existing demosaicing algorithms inapplicable on hardware platforms. In this paper, we introduce a novel low-cost demosaicing algorithm to narrow the gap. We keep the operation window size and computation cost as the first priority and achieve both high PSNR/SSIM and visual perceptual quality compared to previous state-of-the-art methods on standard test datasets. We fully investigate the a priori knowledge of natural scene raw images and find several key cues that are beneficial to demosaicing. Our demosaicing algorithm is a smart fusion of these useful cues. Furthermore, we solve typical demosaicing issues that occur in many traditional methods, including false color artifacts, T-section closing, and zippering artifacts. The proposed method has no learning stage, no iteration operation, a small line buffer and a limited number of parameters, so it can easily be applied to hardware platforms. [ABSTRACT FROM AUTHOR]

Image quality assessment is a critical problem for image compression, which can be utilized as a guidance for image compression and codec evaluation. In this paper, we propose a full reference image quality assessment (IQA) algorithm to measure the perceptual quality of compressed images. The proposed IQA model utilizes a data-driven transform, multi-stage Karhunen-Loeve Transform (MS-KLT), as a feature extractor to decompose both reference and compressed images into feature domain, where the importance of feature distortions in different spectral components to human visual system (HVS) is easy to distinguish. Accordingly, an efficient weighting strategy is proposed to reflect the importance of feature distortions based on the energy of transformed coefficients. Considering HVS characteristics, weighted spatial masking effect is derived from both local and global perspectives. In addition, to avoid influences of random noises, a local adaptive low-pass filtering process is applied as a pre-processing operation. Extensive experimental results on popular datasets show that our proposed method correlates better with the subjective results compared with the state-of-the-art algorithms. Moreover, the proposed method behaves more robustly compared with existing methods, and achieves top-ranking performance on different IQA datasets. [ABSTRACT FROM AUTHOR]

Belief propagation (BP)-based stereo matching has popular owing to its regularity and ability to yield promising results. Some commonly observed hardware-implementation challenges pertaining to the use of this algorithm are large memory requirements and trade-offs between speed and chip area, along with an increasing disparity range. The paper presents a hardware- and memory-efficient architecture for building a BP-based disparity estimation system capable of overcoming issues associated with large disparity ranges. The proposed architecture is memory-efficient owing to the regularity of its underlying algorithm. In addition, the improved hardware efficiency can be attributed to processing element modifications to demonstrate shareable characteristics. Results obtained in this study reveal a 67.8% reduction in required memory corresponding to a time–area term complexity of $O(L(logL)^{2})$ , where L denotes the disparity range. This result is in stark contrast to the $O(L^{2}logL)$ and $O(L^{2})$ complexities observed in extant studies. Compared to state-of-the-art implementations, the proposed architecture offers an 86.2% gate count reduction for message update units at a disparity range of 512. These results confirm the proposed architecture’s suitability for use in large disparity scenarios. [ABSTRACT FROM AUTHOR]

The visual security index (VSI) is a quantized indicator for objective visual security evaluation of selectively encrypted images. One challenging problem in current research is that the performance of VSIs is highly sensitive to the extracted features and the method of similarity measurement, and it is hard to choose appropriate handcrafted features from encrypted images, as well as to find an effective similarity measurement. In this paper, we make the first attempt to present a novel convolutional neural network-based visual security index (CNNVSI). Our proposed CNNVSI is purely data-driven and trained end-to-end. We propose three specialized designs to make the approach work for encrypted low-quality images without any handcrafted features or prior knowledge about the human vision system (HVS). First, we present a patch labeling algorithm to assign each encrypted patch a visual security score. Second, we design a multiscale attention residual network (MARNet) for feature learning. Last, we propose to fuse the learned features from plain images, encrypted images and their discrepancy images. Extensive and systematic experiments are conducted on five publicly available image databases to analyze the performance of our proposed CNNVSI, and the experimental results and their analysis demonstrate that our proposed CNNVSI significantly outperforms the existing state-of-the-art methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can be flexibly deployed to offload cellular traffic or to provide video services for emergency scenarios without infrastructure. However, the inherent resource allocation and three-dimensional (3D) aerial trajectory design have not been formally studied. In this paper, we study the joint resource allocation and 3D aerial trajectory design for dynamic adaptive streaming over HTTP (DASH)-enabled services in a UAV communication system, where a UAV is employed as a base station for multiuser video streaming. Various factors are taken into account, including video data rate, quality variation, communication outage, play interruption, etc. By adopting a video streaming utility model, two fundamental problems are formulated with different practical aims: the first problem maximizes the minimum utility for all users within a given time horizon such that max-min fairness can be provided, and the second problem minimizes the UAV operation time subject to the individual utility requirement for all users to prolong UAV endurance. To tackle the first non-convex problem, we decouple it into three sub-problems, and a three-stage iterative algorithm is proposed to obtain a suboptimal solution by solving the three sub-problems with successive convex approximation and alternating optimization techniques. An exponential search based algorithm is proposed for the second problem by utilizing the structure of the considered problem and a similar three-stage iterative algorithm. Extensive simulations are carried out to evaluate the performance, and the results show that our proposed designs significantly outperform baseline schemes. Furthermore, our results reveal new insights of UAV movement for video streaming and unveil the tradeoff between utility and quality variance. [ABSTRACT FROM AUTHOR]

Linear Regression (LR) is a popular and effective technique in pattern recognition area, which aims to find a transform matrix between source data and target data (usually label matrix). However, a binary zero-one label matrix may be too strict and inappropriate for regression. Besides, directly projecting source data to target data by one transform matrix may lose some intrinsic data information. To address these issues, this paper proposes a novel Pairwise Relations oriented Discriminative Regression (PRDR) method. In PRDR, the source data is regressed into a latent space instead of label space. To supervise the discriminative projection learning, the pairwise relations in source data space and label space are exploited in the latent space simultaneously. The pairwise label relations are transferred into the latent subspace by solving a distance-distance difference minimization problem, and the intraclass instance relations are also preserved in latent space. These two constraints ensure the pairwise similarity of data points after transformation which is beneficial for classification. By further enlarging the margins between true and false classes, PRDR is extended to a robust version, i.e., R-PRDR. An efficient algorithm is presented to solve the PRDR model. Extensive experiments on several popular image datasets demonstrate the effectiveness and efficiency of the proposed method compared with some state-of-the-art regression approaches. [ABSTRACT FROM AUTHOR]

Disparity estimation is an essential task taking part in many light-field applications. Due to the complexity of algorithms and high dimensional property of light-field data, performing this task involves a significant computational effort and results in very long processing time on CPU. Graphics processing units (GPUs), which is capable of massively parallel processing, is a promising solution to cover the computation requirement and speed up the task. In this paper, we develop a GPU-accelerated approach for light-field disparity estimation using a variational computation framework (GVLD). Our algorithm combines the intrinsic sub-pixel precision of variational formulation and the effectiveness of weighted median filtering to produce a highly accurate solution. The proposed algorithm is fully parallelized and optimized for the implementation using the OpenCL framework. An intensive evaluation including a quantitative comparison to related works and a detailed analysis of the proposed approach’s performance is presented. Experimental results demonstrate our superior performance compared to state-of-the-art approaches. The proposed approach is 10+ times faster than other approaches running on a similar GPU platform and provides the most accurate solution among optimization-based approaches. Compared to the implementation running on CPU, our GPU-accelerated method achieves up to $365\times $ speed up. [ABSTRACT FROM AUTHOR]

Social networks are everywhere and currently transmitting very large messages. As a result, transmitting secret messages in such an environment is worth researching. However, the images used in transmitting messages are usually compressed with a JPEG compression channel, which is lossy and damages the transmitted data. Therefore, to prevent secret messages from being damaged, a robust JPEG steganography is urgently needed. In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is first pretrained to fit the transformation relationship between the JPEG image before and after compression by the compression channel. In addition, the BCH encoding is adaptively utilized according to the content of cover image to decrease the error rate of secret message extraction. The DCT (Discrete Cosine Transformation) coefficient adjustment based on practical JPEG channel characteristics further improves the robustness and statistical security. Comparisons with prior state-of-the-art schemes demonstrate that the proposed robust JPEG steganographic algorithm can provide a more robust performance and statistical security. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) clustering is a challenging task due to the complex characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large spectral variability. In this paper, we propose a novel deep spatial-spectral subspace clustering network (DS3C-Net), which explores spatial-spectral information via the multi-scale auto-encoder and collaborative constraint. Considering the structure correlations of HSI, the multi-scale auto-encoder is first designed to extract spatial-spectral features with different-scale pixel blocks which are selected as the inputs. Then, the collaborative constrained self-expressive layers are introduced between the encoder and decoder, to capture the self-expressive subspace structures. By designing a self-expressiveness similarity constraint, the proposed network is trained collaboratively, and the affinity matrices of the feature representation are learned in an end-to-end manner. Based on the affinity matrices, the spectral clustering algorithm is utilized to obtain the final HSI clustering result. Experimental results on three widely used hyperspectral image datasets demonstrate that the proposed method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In many practical engineering applications, the number of actions that have been finished should be known, particularly for an untrimmed video sequence that includes an event with a series of actions, it is important to know the number of actions that have been finished. In this paper, we termed this process as visual event progress estimation (EPE). However, the research related to this problem is few in the research community. To solve this problem, a visual human action analysis-based framework, namely one-shot simultaneously action detection and identification (SADI)-EPE, is presented in this paper. The visual EPE is modeled as an online one-shot learning-based problem; sliding window and attention-based bag of key poses formulate our framework. Unlike most of the action analysis methods relying on a number of training data of some predefined classes, our method can realize SADI for any event if one sample of the event is given, which makes it feasible for practical applications. At the same time, not only SADI but also the progress estimation of the event can be realized by our algorithm. In terms of methodology, the key pose is defined by an invariant pose descriptor from skeletal data and silhouette data. Moreover, in order to extract representative and discriminative poses from one training sample, we present a new bidirectional $k$ NN-based attention weighted key pose selection method, which can filter the unrelated actions and model different importance of various key poses. In addition, an attention-based multi-modal fusion scheme, which addresses the difficulty of high-dimensional features and few training samples, is proposed to augment the performance of our algorithm. Finally, we propose an evaluation criterion for the estimation problem. Extensive results demonstrated the efficacy of our proposed framework. [ABSTRACT FROM AUTHOR]

The classical solution to an underdetermined system of linear equations mainly has two opposite directions, which lead to either a large l2-norm sparse solution or a non-sparse minimum l2-norm solution. In this paper, we systematically show that by modifying the well-known basic matching pursuit algorithm originally proposed to identify the sparse solution, an alternative solution between the two classical ones could be obtained. The modified algorithm, termed as random matching pursuit (RMP), is then used to create a novel image watermarking framework. Compared to conventional systems, the security is substantially improved by the use of random over-complete dictionaries and the order parameter of RMP. Capacity can also be increased thanks to the transform with over-complete dictionaries that could expand signal dimension. Meanwhile, imperceptibility and robustness properties of the proposed design framework are not compromised. The classical spread spectrum and improved spread spectrum techniques are applied to the proposed framework for practical implementations. The novelty and effectiveness of the proposed systems are supported by rigorous performance analysis and experimental results using an image data set. This paper reveals the potential of using over-complete dictionaries in multimedia watermarking systems, which theoretically leads to the exploration of alternative candidates among the infinite solutions to underdetermined linear systems other than minimum l2-norm and sparse ones. [ABSTRACT FROM AUTHOR]

The spatial resolution of a depth-acquisition device, such as a Light Detection and Ranging (LiDAR) sensor, is limited because of the slow acquisition. To accurately reconstruct a depth image from limited spatial resolution, a two-stage sampling process has been widely used. However, two-stage sampling uses an irregular sampling pattern for the sampling operation, which requires complex computation for reconstruction and additional memory space for storage. A mathematical formulation of a LiDAR system demonstrates that two-stage sampling does not satisfy its timing constraint for practical use. To overcome the drawbacks of two-stage sampling, this paper proposes a new sampling method that reduces the computational complexity and memory requirements by generating the optimal representatives of a sampling pattern in down-sample data. A sampling pattern can be derived from a $k$ -NN expanding operation from the down-sampled representatives. The proposed algorithm is designed to preserve the object boundary by restricting the expansion-operation only to the object boundary or complex texture. In addition, the proposed algorithm runs in linear-time complexity and reduces the memory requirements using a down-sampling ratio. The experimental results demonstrate that the proposed sampling outperforms grid sampling by at most 7.92 dB. Consequently, the proposed sampling achieves reconstructed quality similar to that of optimal sampling, while substantially reducing the computation time and memory requirements. [ABSTRACT FROM AUTHOR]

In this paper, a new metric learning algorithm is proposed to improve face verification and person re-identification in RGB images by learning from RGB and Depth (RGB-D) training images. We address this problem by formulating it as a Learning Using Privileged Information problem, in which the additional depth images associated with the RGB training images are not available for the testing process. Based on the large margin nearest neighbor (LMNN) classification framework, we propose an effective metric learning method called large margin nearest neighbor classification with privileged information (LMNN+) by incorporating depth information to improve decision function learning in the training process. Specifically, two distance metrics based on visual features as well as depth features are jointly learned by minimizing the triplet loss in which the within-class difference is minimized, while the between-class difference is maximized. The distances in the depth feature space can be utilized to guide the training process in the visual feature space. In addition, we propose an efficient optimization method which can handle billions of constraints in the optimization problem of LMNN+. The comprehensive experiments on the EUROCOM data set, the CurtinFaces data set as well as the BIWI RGBD-ID data set demonstrate the effectiveness of our algorithm for face verification and person re-identification by leveraging privileged information. [ABSTRACT FROM AUTHOR]

Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understanding and describing the visual contents. Recent researches have devoted great efforts on the task of rain removal for improving the image visibility. However, there is very few exploration about the quality assessment of de-rained image, even it is crucial for accurately measuring the performance of various de-raining algorithms. In this paper, we first create a de-raining quality assessment (DQA) database that collects 206 authentic rain images and their de-rained versions produced by 6 representative single image rain removal algorithms. Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images. To quantitatively measure the quality of de-rained image with non-uniform artifacts, we propose a bi-directional feature embedding network (B-FEN) which integrates the features of global perception and local difference together. Experiments confirm that the proposed method significantly outperforms many existing universal blind image quality assessment models. To help the research towards perceptually preferred de-raining algorithm, we will publicly release our DQA database and B-FEN source code on https://github.com/wqb-uestc. [ABSTRACT FROM AUTHOR]

Semi-global matching (SGM) is favored for high accuracy real-time stereo matching design as it achieves a good trade-off between disparity image quality and computational complexity. Nevertheless, most of previous SGM designs so far are restricted to the real-time processing of small image resolution and disparity range, or achieve high throughput by simplifying the original algorithm at the penalty of significant disparity image quality degradation. We analyze that the major challenge to efficient SGM design is its memory architecture, including both on-chip memory cost and off-chip memory bandwidth. We address the memory architecture challenge by algorithm and architecture co-design. Based on two observed features of SGM algorithm, i.e. incompleteness and inaccuracy, this paper proposes several efficient techniques to reduce on-chip memory cost and compress off-chip memory bandwidth respectively. Moreover, we also design high throughput and pipelined architecture to implement the proposed techniques. The disparity image quality and hardware efficiency of the proposed SGM design are evaluated on both KITTI2015 and Middlebury V3 stereo datasets. Evaluation results demonstrate that, the throughput of the proposed circuit designs can easily achieve 1080P@30fps at the disparity range of 128, and can reduce the on-chip memory cost and off-chip memory bandwidth by up to $4\times $ and $2\times $ respectively while achieving better or the same disparity image quality, compared with the best reference design techniques. [ABSTRACT FROM AUTHOR]

This paper designs a novel underpainting based camera shooting resilient (CSR) document watermarking algorithm for dealing with the leak source tracking problem. By applying such algorithm, we can extract the authentication watermark information from the candid photographs. The watermarked underpainting contains three significant properties. 1) Inconspicuousness. The watermarked underpainting is inconspicuous and it will not easily be maliciously attacked. 2) Robustness. We propose DCT-based watermark embedding algorithm and distortion compensation based extracting algorithm, which make the watermark robust to camera shooting process. 3) Autocorrelation. We design the flip-based method to arrange the watermarked underpainting. So that a complete watermark region can be accurately located even if part of the document is recorded. Compared with previous watermarking algorithms, the proposed scheme guaranteed content independent embedding as well as the robustness to the camera shooting process. Besides, the proposed scheme satisfies the accuracy of extraction even when the captured document is incomplete. [ABSTRACT FROM AUTHOR]

This paper presents a method to improve the traditional mixture of deformable part models (MDPM) method from the learning perspective. First, an object part configuration learning algorithm based on group sparsity constraint is introduced to automatically discover the object part number, size, and location. The algorithm imposes two additional regularization terms in addition to the standard hinge loss function. The first term focuses on automatic part selection and the second term focuses on automatic part placement. Second, this paper introduces an improved MDPM training framework. The framework applies a learned transformation to normalize the prediction score from each individual deformable part model (DPM) into a pseudo probability such that the partition of the entire object appearance feature space becomes less sensitive to the prior distributions of different DPMs. Finally, the two proposed improvements are combined and formulated under the expectation-maximization framework. We evaluate our method mainly using the PASCAL VOC2007 and VOC2010 detection benchmarks and show that the proposed learning algorithms could increase the detection mean AP score by 2.4% and 0.9%, respectively, on these two data sets when using the proposed part selection method and the training algorithm. We also present further in-depth analysis of the proposed algorithm in the experiments. [ABSTRACT FROM PUBLISHER]

Person attribute recognition aims to identify the attribute labels from the pedestrian images. Extracting contextual relation from the images and attributes, including the spatial-semantic relations, the spatial context and the semantic correlation, is beneficial to enhance the discrimination of the features for recognizing the attributes. Thus, this work proposes a sequence contextual relation learning (SCRL) method to capture these relations. It first embeds the images and attributes into sequences in two branches. Then SCRL flexibly learns the contextual relation from the sequences with the parallel attention model structure, which integrates the inter-attention and intra-attention models. The inter-attention module is utilized to extract the spatial-semantic relations, while the intra-attention is designed to gain the spatial context and the semantic correlation. Both attention modules are comprised of several parallel attention units and each unit can obtain the pairwise relations in one subspace. Therefore, they obtain the relations in multiple subspaces, which can improve the comprehensiveness of the relation learning. Additionally, for the sake of better extraction of spatial-semantic relations, this paper employs connectionist temporal classification (CTC) loss which is capable of driving the network to enforce monotonic alignment between the image and attribute. It can also accelerate the convergence of the network by the algorithm in it. Extensive experiments on five public datasets, i.e., Market-1501 attribute, Duke attribute, PETA, RAP and PA-100K datasets, demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

High Efficiency Video Coding (HEVC) involves variable size higher order Discrete Cosine Transform (DCT) to increase coding efficiency. However, higher order DCT increases the hardware complexity, thereby increasing the area and power consumption of the physical design. In this paper, a new method to derive higher order DCT is presented. The proposed method uses rotation based structures and uses trigonometric relations to decompose those structures. Finally, all the floating point coefficients of the rotation unit are approximated by dyadic values such that they can be realized by add and shift operation. Hence, the proposed method eliminates the need for multipliers and significantly reduces the complexity of the core transform for HEVC. The proposed approach is used to design all size of DCT supported by HEVC and can be further extended to design larger size DCT and IDCT to be used in the near future for image and video compressions. The proposed architecture has a maximum throughput of 3.2 Gsps and is capable of processing at least 30 frames/s of ultra high definition $3840\times 2160$ video. Experimental results demonstrate that the proposed design method maintains similar coding efficiency, but requires 24% less area-delay product and 25% less power compared to the HEVC reference algorithm. [ABSTRACT FROM AUTHOR]

With the development of virtual reality (VR) technology, the future of VR systems is evolving from single-user wired connections to multi-user wireless connections. However, wireless online rendering and transmission incur extra processing and transmission latency, as well as higher bandwidth requirements. To meet the requirements of wireless VR applications and enhance the quality of the VR user experience, this paper designs a view synthesis-based 360° VR caching system over Cloud Radio Access Network (C-RAN), where both mobile edge computing (MEC) and hierarchical caching are supported. In the system, an MEC-Cache Server is deployed in the pooled Base band Units (BBU pool) and used for view synthesis and caching. In addition, the remote radio heads (RRHs) can also cache some video contents. If the requested content of a specific view is cached in the BBU pool or RRHs, or can be synthesized with the aid of the cached adjacent views, it is unnecessary to request the content from the remote VR video source server. Therefore, the transmission latency and backhaul traffic load for VR services can be decreased. We formulate a hierarchical collaborative caching problem aiming to minimize the transmission latency, which is proved NP-hard. To address the impractical expenses of the offline optimal method, an online MaxMinDistance caching algorithm with low complexity is proposed. Numerical simulation results demonstrate that the proposed caching strategy provides significantly improved cache hit rate, backhaul traffic load, transmission latency, and Quality of Experience (QoE) performances relative to conventional caching strategies. [ABSTRACT FROM AUTHOR]

Graph based classification methods have been widely applied in the fields of computer vision and machine learning. The quality of the graph highly affects the performance of these methods. The same object is commonly represented by different features, i.e., multi-view features, which leads to multiple graphs corresponding to different features in multi-view learning. However, what kind of graph is important for the task is unknown in advance. Moreover, existing multi-view learning methods become weak in dealing with noisy graphs when the data is corrupted by the noise. In this paper, we address this problem by observing that the noise of each graph has specific structure. Then, based on this observation we propose a robust multi-view graph recovery (RMGR) method in which the specific structure is used to clean the multiple input noisy graphs and these cleaned graphs are simultaneously aggregated into a consensus graph by adaptively assigning great weighted coefficients for important graphs. To make the consensus graph suit classification, the clustering structure is introduced to restrain the rank of Laplacian matrix of the consensus graph such that the number of its connected components is equal to that of clustering. In doing so, the graph is adaptively adjusted during optimization to more accurately partition data. The optimization problem is solved by proposed the iterative update algorithm. Extensive experiments on synthetic and several benchmark data sets show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Some methods developed in reversible data hiding (RDH) make use of prediction for data embedding for original pixel estimation. Predicators may also be exploited in RDH in encrypted image (RDHEI); this has become a research interest in recent years because of the development of cloud computing and a need for content owner privacy. This paper presents a high-capacity reversible data hiding in encrypted image (RDHEI) that employs local difference predictor. In this algorithm, an image is divided into non-overlapping blocks. In each block, the central pixel of the block is considered as the leader pixel and others as follower ones. The prediction errors between the intensity of the follower pixels and leader one are calculated using local difference predictor and analyzed to determine a label for block embedding capacity. This label indicates the amount of data that can be embedded in a block after encryption. Using this pre-processing for all blocks, we vacate rooms before the encryption of the original image to achieve high embedding capacity. Also, using these labels, embedded data is extracted and the original image is losslessly reconstructed at the decoding phase. Comparing to existent RDHEI algorithms, not only embedding capacity is increased by the proposed algorithm, but also a perfect reconstruction of the original image is realized by content owner without having data hider key. Experimental results confirm that the proposed algorithm outperforms state of the art RDHEI methods. [ABSTRACT FROM AUTHOR]

Recently, a method based on multiple histograms modification (MHM) is proposed for reversible data hiding (RDH), in which a sequence of prediction-error histograms are generated and two expansion bins are selected in each histogram for expansion embedding. However, although efficient, it only chooses a single pair of expansion bins which limits the embedding capacity. On the other hand, the exhaustive expansion-bin-selection procedure in MHM takes huge computation time, so that it cannot be extended for high capacity RDH. In order to overcome the aforementioned drawbacks, an optimal RDH scheme based on MHM for high capacity embedding is proposed in this paper. First, to improve the embedding capacity, instead of a single pair of expansion bins, multiple pairs of expansion bins are utilized for each histogram, and the multiple-expansion-bin-selection for optimal embedding is formulated as an optimization problem. Then, unlike the exhaustive searching way used in MHM, a computationally efficient algorithm is proposed to solve the optimization problem, so that the optimal expansion bins can be adaptively determined to optimize the embedding performance. By the proposed approach, high embedding capacity can be achieved with good marked image quality, and the experimental results show that it is better than the original MHM and some other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Video segmentation is a task of partitioning pixels that exhibit homogeneous appearance and motion into coherent spatial–temporal groups, which is still challenging for aerial applications. In this paper, a principled combination of superpixel labeling priors and Markov random field (S-MRF) is proposed for aerial video segmentation. The proposed approach has several contributions: 1) we develop a metadata-based global projection model with coordinate transformation to estimate motion information between frames; 2) the superpixel labeling priors from previous frames are incorporated into the segmentation of the current frame, leading to a highly efficient probabilistic label propagation algorithm; and 3) we perform an MRF optimization on the initial segments with propagated labeling priors to improve the temporal coherency. In addition, a new video dataset is collected and will be made publicly available to evaluate the performance of aerial video segmentation algorithms. The experimental results show that the proposed approach outperforms the state-of-the-art video segmentation methods. [ABSTRACT FROM AUTHOR]

Joint sparse representation (JSR) model has recently emerged as a powerful technique with wide variety of applications. In this paper, the JSR model is extended to error concealment (EC) application, being effective to recover the original image from its corrupted version. This model is based on jointly learning a dictionary pair and two mapping matrices that are trained offline from external training images. Given the trained dictionaries and mappings, the restoration is done by transferring the recovery problem into the sparse representation domain with respect to the trained dictionaries, which is further transformed into a common space using the respective mapping matrices. Then, the reconstructed image is obtained by back projection into the spatial domain. In order to improve the accuracy and stability of the proposed JSR-based EC algorithm and avoid unexpected artifacts, the local and non-local priors are seamlessly integrated into the JSR model. The non-local prior is based on the self-similarity within natural images and helps to find an accurate sparse representation by taking a weighted average of similar areas throughout the image. The local prior is based on learning the local structural regularity of the natural images and helps to regularize the sparse representation, exploiting the strong correlation in the small local areas within the image. Compared with the state-of-the-art EC algorithms, the results show that the proposed method has better reconstruction performance in terms of objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

Most existing algorithms for depth estimation from single monocular images need large quantities of metric ground-truth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and can be easily obtained from vast stereo videos. Acquiring metric depths from stereo videos are sometimes impracticable due to the absence of camera parameters. In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm. We introduce a new “relative depth in stereo” (RDIS) dataset densely labeled with relative depths. We first pretrain a ResNet model on our RDIS dataset. Then, we finetune the model on RGB-D datasets with metric ground-truth depths. During our finetuning, we formulate depth estimation as a classification task. This re-formulation scheme enables us to obtain the confidence of a depth prediction in the form of probability distribution. With this confidence, we propose an information gain loss to make use of the predictions that are close to ground-truth. We evaluate our approach on both indoor and outdoor benchmark RGB-D datasets and achieve the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

It is recognized that existing multi-scale exposure fusion algorithms can be improved using edge-preserving smoothing techniques. However, the complexity of edge-preserving smoothing-based multi-scale exposure fusion is an issue for mobile devices. In this paper, a simpler multi-scale exposure fusion algorithm is designed in YUV color space. The proposed algorithm can preserve details in the brightest and darkest regions of a high dynamic range (HDR) scene and the edge-preserving smoothing-based multi-scale exposure fusion algorithm while avoiding color distortion from appearing in the fused image. The complexity of the proposed algorithm is about half of the edge-preserving smoothing-based multi-scale exposure fusion algorithm. The proposed algorithm is thus friendlier to the smartphones than the edge-preserving smoothing-based multi-scale exposure fusion algorithm. In addition, a simple detail-enhancement component is proposed to enhance fine details of fused images. The experimental results show that the proposed component can be adopted to produce an enhanced image with visibly enhanced fine details and a higher MEF-SSIM value. This is impossible for existing detail enhancement components. Clearly, the component is attractive for PC-based applications. [ABSTRACT FROM AUTHOR]

Images captured in low lighting environment suffer from both low luminance contrast and noise corruption. However, most existing contrast enhancement algorithms only consider contrast boosting, which tends to reveal or amplify noise that is originally not visible in the dark areas. In this paper, we propose a joint contrast enhancement and denoising algorithm, which is based on structure/texture layer decomposition via minimization of dual forms of graph total variation (GTV). Specifically, the structure layer is expected to be generally smoothing but with sharp edges at the foreground background boundaries, for which we propose a quadratic form of GTV (QGTV) as the prior that promotes signal smoothness along graph structure. For the texture layer, a re-weighted GTV (RGTV) is tailored to noise removal while preserving true image details. We provide theoretical analysis about the filtering behavior of these two priors. Furthermore, a boost factor is derived per patch via optimal contrast-tone mapping to improve the overall brightness level of the patch. Finally, an optimization objective function is formulated, which casts image decomposition, brightness boosting, and noise reduction into a unified optimization framework. We further propose a fast approach to efficiently solve the optimization and provide analysis about the convergency. The experimental results show that the proposed method outperforms the state-of-the-art works in subjective, objective, and statistical quality evaluation. [ABSTRACT FROM AUTHOR]

The size of a high-density-camera-array (HDCA)-based light field image (LFI) is usually very large, containing hundreds of high-resolution views. Therefore, there is an urgent need to efficiently compress it. Currently, no compression algorithms, specially, for the HDCA-based LFI have been designed. In this paper, we propose an algorithm based on a quadtree-based 2D hierarchical coding framework for the HDCA-based LFI data compression. The proposed framework has the following contributions. First, we organize the views of the HDCA-based LFI into a quadtree-based coding structure. Under this structure, all of the views are divided into four quadrants at the first level. Each quadrant is further sub-divided into four quadrants at each subsequent level. The process continues until the desired depth is reached. This quadtree-based coding structure can make full use of the strong inter-view correlations to improve the coding efficiency. In addition, the proposed quadtree-based structure can be easily extended to a general 2D hierarchical structure with variable group of pictures (GOP) sizes to adapt to the reference frame buffer constraint. Second, we try to improve the performance of the 2D hierarchical coding framework using the distance-based criteria for both the reference frame selection and motion vector scaling. Third, a one-pass optimal bit allocation scheme is proposed to further optimize the performance by taking the quality dependencies among various views into consideration. The proposed framework is implemented in the newest video coding standard, high efficiency video coding (HEVC). The experimental results show that the proposed quadtree-based 2D hierarchical coding framework can achieve an average of over 25% bitrate saving compared with the 1D hierarchical coding structure. [ABSTRACT FROM AUTHOR]

In the classical bilateral filter, a range kernel is used together with a spatial kernel for smoothing out fine details while simultaneously preserving edges. More recently, it has been demonstrated that even coarse textures can be smoothed using joint bilateral filtering. In this paper, we demonstrate that the superior texture filtering results can be obtained by adapting the spatial kernel at each pixel. To the best of our knowledge, spatial adaptation (of the bilateral filter) has not been explored for texture smoothing. The rationale behind adapting the spatial kernel is that one cannot smooth beyond a certain level using a fixed spatial kernel, no matter how we manipulate the range kernel. In fact, we should simply aggregate more pixels using a sufficiently wide spatial kernel to locally enhance the smoothing. Based on this reasoning, we propose to use the classical bilateral filter for texture smoothing, where we adapt the width of the spatial kernel at each pixel. We describe a simple and efficient gradient-based rule for the latter task. The attractive aspect is that we are able to develop a fast algorithm that can accelerate the computations by an order without visibly compromising the filtering quality. We demonstrate that our method outperforms classical bilateral filtering, joint bilateral filtering, and other filtering methods, and is competitive with the optimization methods. We also present some applications of texture smoothing using the proposed method. [ABSTRACT FROM AUTHOR]

The matrix decomposing into a sum of low-rank and sparse components has found extensive applications in many areas including video surveillance, computer vision, and medical imaging. In this paper, we propose a new algorithm for recovery of low rank and sparse components of a given matrix. We have also proved the convergence of the proposed algorithm. The simulation results with synthetic and real signals such as image and video signals indicate that the proposed algorithm has a better performance with lower run-time than the conventional methods. [ABSTRACT FROM AUTHOR]

Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation. Inspired by the prevalent self-example prior in image super-resolution, in this paper, we observe that a coarse enough image down-sampled from a blurry observation is approximately a low-resolution version of the latent sharp image. We prove this phenomenon theoretically and define the coarse enough image as a latent structure prior of the unknown sharp image. Starting from this prior, we propose to restore sharp images from the coarsest scale to the finest scale on a blurry image pyramid and progressively update the prior image using the newly restored sharp image. These coarse-to-fine priors are referred to as multi-scale latent structures (MSLSs). Leveraging the MSLS prior, our algorithm comprises two phases: 1) we first preliminarily restore sharp images in the coarse scales and 2) we then apply a refinement process in the finest scale to obtain the final deblurred image. In each scale, to achieve lower computational complexity, we alternately perform a sharp image reconstruction with fast local self-example matching, an accelerated kernel estimation with error compensation, and a fast non-blind image deblurring, instead of computing any computationally expensive non-convex priors. We further extend the proposed algorithm to solve more challenging non-uniform blind image deblurring problem. The extensive experiments demonstrate that our algorithm achieves the competitive results against the state-of-the-art methods with much faster running speed. [ABSTRACT FROM AUTHOR]

Dictionary learning has emerged as a powerful tool for a range of image processing applications and a proper dictionary always plays a key issue to the final achievable performance. In this paper, a class-oriented discriminative dictionary learning (CODDL) method is presented for image classification applications. It takes a comprehensive consideration of multiple optimization objectives, emphasizing class discrimination of both dictionary atoms and representation coefficients. The atoms of the learned dictionary should be grouped into class level sub-dictionaries. Meanwhile, the sparse representation coefficients of an input sample should be concentrated on the sub-dictionary of the class it belongs to. Then, based on the learned class-oriented discriminative dictionary, the structured representation coefficients can thus be used for image classification with a simple and efficient classification scheme. The superior performance of the proposed algorithm is demonstrated through extensive experiments. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates a novel algorithm for streaming video over HTTP. The problem is formulated as a non-convex optimization problem which is constrained by the predicted available bandwidth, chunk deadlines, available video rates, and buffer occupancy. The objective is to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among different chunks for the minimum re-buffering time. We propose FastScan, a low complexity algorithm that solves the problem. The online adaptations for dynamic bandwidth environments are proposed with imperfect available bandwidth prediction. The results of experiments driven by variable bit rate (VBR) encoded video, video platform system (dash.js), and cellular bandwidth traces of a public dataset reveal the robustness of the online version of FastScan algorithm and demonstrate its significant performance improvement as compared to the considered state-of-the-art video streaming algorithms. For example, on an experiment conducted over 100 real cellular available bandwidth traces of a public dataset that spans different available bandwidth regimes, our proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to Bola, Festive, BBA, RB, FastMPC, and Pensieve algorithms. [ABSTRACT FROM AUTHOR]

Recovering low-rank and sparse components from missing observations is an essential problem in various fields. In this paper, we have proposed a method to address the missing low-rank and sparse decomposition problem. We have used the smoothed nuclear norm and the $L_{1}$ norm to impose the low-rankness and sparsity constraints on the components, respectively. Furthermore, we have suggested a linear modeling for the corrupted observations. The problem has been solved with the aid of alternating minimization. Moreover, some simplifications have been applied to the relations to reduce the computational complexity, which makes the algorithm suitable for large-scale problems. To evaluate the proposed method, different simulation scenarios have been devised. The superiority of the suggested scheme over its counterparts has been confirmed on both the recovery accuracy and the convergence speed in various applications. [ABSTRACT FROM AUTHOR]

Recently, sparse representation-based classification (SRC) has been widely studied and has produced state-of-the-art results in various classification tasks. Learning useful and computationally convenient representations from complex redundant and highly variable visual data is crucial for the success of SRC. However, how to find the best feature representation to work with SRC remains an open question. In this paper, we present a novel discriminative projection learning approach with the objective of seeking a projection matrix such that the learned low-dimensional representation can fit SRC well and that it has well discriminant ability. More specifically, we formulate the learning algorithm as a bilevel optimization problem, where the optimization includes an $\ell _{1}$ -norm minimization problem in its constraints. Through the bilevel optimization model, the relationship between sparse representation and the desired feature projection can be explicitly exploited during the learning process. Therefore, SRC can achieve a better performance in the transformed subspace. The optimization model can be solved by using a stochastic gradient ascent algorithm, and the desired gradient is computed using implicit differentiation. Furthermore, our method can be easily extended to learn a dictionary. The extensive experimental results on a series of benchmark databases show that our method outperforms many state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel superpixel segmentation method using an adaptive nonlocal random walk (ANRW) algorithm. There are three main steps in our image superpixel segmentation algorithm. Our method is based on the random walk model, in which the seed points are produced to generate the initial superpixels by a gradient-based method in the first step. In the second step, the ANRW is proposed to get the initial superpixels by adjusting the NRW to obtain a better image and superpixel segmentation. In the last step, these small superpixels are merged to get the final regular and compact superpixels. The experimental results demonstrate that our method achieves a better superpixel performance than the state-of-the-art methods. Our source code will be available at: http://github.com/shenjianbing/ANRW. [ABSTRACT FROM AUTHOR]

Along with the rapid increase in the availability of high-quality video formats such as high definition (HD), ultra HD, and high dynamic range, a huge demand for data rates during their transmission has become inevitable. Consequently, the role of video compression techniques has become crucially important in the process of mitigating the data rate requirements. Even though the latest video codec high efficiency video coding (HEVC) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/advanced video coding, the HEVC coded videos in the meantime have become even more vulnerable to network impairments. Therefore, it is equally important to assess the consumers’ perceived quality degradation prior to transmitting HEVC coded videos over an error-prone network, and to include error resilient features so as to minimize the adverse effects of those impairments. To this end, this paper proposes a probabilistic model which accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP– $\lambda $ relationship which can be used in the rate-distortion optimization (RDO) process. During the derivation process of the probabilistic model, the impacts from the motion vectors, the pixels in the reference frames, and the clipping operations are accounted, and consequently, the model is capable of minimizing the prediction error to as low as 3.11%, whereas the state-of-the-art methods cannot reach below 20.08%, under identical conditions. Furthermore, the enhanced RDO process has resulted in 21.41%–43.59% improvement in the BD-rate compared to the state-of-the-art error resilient algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lossy image compression algorithm called microshift. We employ an algorithm-hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, and then the sub-quantized values are further compressed. Two methods, FAST and MRF models, are proposed to recover the bitdepth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. On an average, our compression algorithm can compress images to 1.25-bits per pixel with a resulting quality that outperforms the state-of-the-art on-chip compression algorithms in both peak signal-to-noise ratio and structual similarity. Then, we propose a hardware architecture and implement the algorithm on an FPGA. The results on the ASIC design further validate the low-hardware complexity and high-power efficiency, showing that our method is promising, particularly for low-power wireless vision sensor networks. [ABSTRACT FROM AUTHOR]

The growing demand for high-performance ultra-high-definition video coding leads to H.265/high-efficiency video coding (HEVC), where the increased computational complexity and data/timing dependence hinder its coding throughput. To address these challenges, this paper presents four algorithm adaptations and a fully parallel hardware architecture for an H.265/HEVC intra encoder. To the best of our knowledge, this is the first fully parallel H.265/HEVC intra encoder. This design supports 35 prediction modes and all coding tree unit partitions. All PUs are independently processed in four prediction engines for high parallelism. An appropriate set of intra prediction modes, RDO candidates, and CABAC rate estimate instances is assigned to each prediction engine, where internal computational tasks are pipelined and scheduled to maximize the processing throughput. Compared with the HM-15.0 software, the proposed algorithm adaptations lead to a reduction of 27% in computational workload, while the average BD-rate and BD-PSNR are 4.39% and −0.21 dB, respectively. This BD-rate is lower than the existing designs with the same video resolution. FPGA implementation of the proposed design shows that it operates at 120 MHz and supports 45 fps of 1080P video sequences using 201-K logic elements and 120-KB on-chip SRAM. ASIC implementation of the proposed design in TSMC 90-nm technology shows that its clock frequency reaches 320 MHz with a hardware gate count of 2288 K, and that it supports real-time encoding of 30 fps of 4-K video sequences. Compared with the state-of-the-art designs, our proposed design demonstrates advantages in computational complexity, bit rate, video quality, throughput, reliability, and flexibility. [ABSTRACT FROM AUTHOR]

This paper presents the VLSI architecture and hardware implementation of a highly efficient deblocking filter (DBF) for High Efficiency Video Coding systems. In order to reduce the number of data accesses and thus to enhance the timing efficiency, novel data structures and memory access schemes for image pixels are proposed. Furthermore, a novel edge-fetching order is presented to strike a balance between the processing throughput and complexity. Based on the proposed structure and access pattern, a six-stage pipelined two-line DBF engine with low-latency data access sequence is designed, aiming to achieve high processing throughput while at the same time maintaining low complexity. The detailed storage structure and data access scheme are illustrated and VLSI architecture for the DBF engine is depicted in this paper. In addition, the proposed DBF is implemented using TSMC 90-nm standard cell library. The experimental results based on postlayout estimations show that the proposed design can achieve 60 frames/s for a frame resolution of $4096\times 2048$ pixels (ultra high definition resolution) assuming an operating frequency of 100 MHz. Moreover, this design occupies an area complexity of 466.5 kGE with a power consumption of 26.26 mW. In comparison with prior designs targeting similar system specification and throughput, the proposed design results in a significantly reduced area complexity. [ABSTRACT FROM PUBLISHER]