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Deep neural networks have achieved great performance on blind Image Quality Assessment (IQA), but it is still challenging for using one network to accurately predict the quality of images with different distortions. In this paper, a Distortion-Aware Convolutional Neural Network (DACNN) is proposed for blind IQA, which works effectively for not only synthetically distorted images but also authentically distorted images. The proposed DACNN consists of a distortion aware module, a distortion fusion module, and a quality prediction module. In the distortion aware module, a Siamese network-based pretraining strategy is proposed to design a synthetic distortion-aware network for full learning the synthetic distortions, and an authentic distortion-aware network is used for extracting the authentic distortions. To efficiently fuse the learned distortion features, and make the network pay more attention to the essential features, a weight-adaptive fusion network is proposed to adaptively adjust the weight of each distortion. Finally, the quality prediction module is adopted to map the fused features to a quality score. Extensive experiments on four authentic IQA databases and four synthetic IQA databases have proved the effectiveness of the proposed DACNN. [ABSTRACT FROM AUTHOR]

In the three-dimensional video system, the depth image-based rendering is a key technique for generating synthesized views, which provides audiences with depth perception and interactivity. However, the inaccuracy of depth information leads to geometrical rendering position errors, and the compression distortion of texture and depth videos degrades the quality of the synthesized views. Although existing quality enhancement methods can eliminate the distortions in the synthesized views, their huge computational complexity hinders their applications in real-time multimedia systems. To this end, a residual distillation enhanced network (RDEN)-guided lightweight synthesized view quality enhancement (SVQE) method is proposed to minimize holes and compression distortions in the synthesized views while reducing the model complexity. First, a rethinking on the deep-learning-based SVQE methods is performed. Then, a feature distillation attention block is proposed to effectively reduce the distortions in the synthesized views and make the model fulfill more real-time tasks, which is a lightweight and flexible feature extraction block using an information distillation mechanism and a lightweight multi-scale spatial attention mechanism. Third, a residual feature fusion block is proposed to improve the enhancement performance by using the feature fusion mechanism, which efficiently improves the feature extraction capability without introducing any additional parameters. Experimental results prove that the proposed RDEN efficiently improves the SVQE performance while consuming few computational complexities compared with the state-of-the-art SVQE methods. [ABSTRACT FROM AUTHOR]

The depth video contains large smooth contents with sharp edges. Since the deep learning-based color video orientated intra prediction methods pay no attention to the characteristics of depth video, they are unsuitable for optimizing the coding efficiency of depth video. In this paper, a multiple resolution prediction method with deep up-sampling is proposed to promote the coding efficiency of depth video. To efficiently encode the depth blocks of different complexity, the depth block is selectively encoded at different resolutions, including $\times 1$ , $\times 1$ /2, and $\times 1$ /4 resolutions. If the block is encoded with a low-resolution (LR), the resolution of reconstructed LR depth block is recovered by an up-sampling network. To constrain the quality of both reconstructed high-resolution depth block and its synthesized view, a view synthesis distortion guidance mechanism is proposed for the up-sampling network. In addition, a distillation-based lightweight up-sampling network is proposed to reduce the computational complexity. Experimental results demonstrate that the proposed multiple resolution prediction method obtains an average of 10.84% BD-rate saving in comparison with 3D-HEVC. [ABSTRACT FROM AUTHOR]

Multiview video coding (MVC) aims to compress the multiview video through the elimination of video redundancies, where the quality of the reference frame directly affects the compression efficiency. In this paper, we propose a deep virtual reference frame generation method based on a disparity-aware reference frame generation network (DAG-Net) to transform the disparity relationship between different viewpoints and generate a more reliable reference frame. The proposed DAG-Net consists of a multi-level receptive field module, a disparity-aware alignment module, and a fusion reconstruction module. First, a multi-level receptive field module is designed to enlarge the receptive field, and extract the multi-scale deep features of the temporal and inter-view reference frames. Then, a disparity-aware alignment module is proposed to learn the disparity relationship, and perform disparity shift on the inter-view reference frame to align it with the temporal reference frame. Finally, a fusion reconstruction module is utilized to fuse the complementary information and generate a more reliable virtual reference frame. Experiments demonstrate that the proposed reference frame generation method achieves superior performance for multiview video coding. [ABSTRACT FROM AUTHOR]

In video coding, it is a challenge to deal with scenes with complex motions, such as rotation and zooming. Although affine motion compensation (AMC) is employed in Versatile Video Coding (VVC), it is still difficult to handle non-translational motions due to the adopted hand-craft block-based motion compensation. In this paper, we propose a deep affine motion compensation network (DAMC-Net) for inter prediction in video coding to effectively improve the prediction accuracy. To the best of our knowledge, our work is the first attempt to deal with the deformable motion compensation based on CNN in VVC. Specifically, a deformable motion-compensated prediction (DMCP) module is proposed to compensate the current encoding block through a learnable way to estimate accurate motion fields. Meanwhile, the spatial neighboring information and the temporal reference block as well as the initial motion field are fully exploited. By effectively fusing the multi-channel feature maps from DMCP, an attention-based fusion and reconstruction (AFR) module is designed to reconstruct the output block. The proposed DAMC-Net is integrated into VVC and the experimental results demonstrate that the proposed method considerably enhances the coding performance. [ABSTRACT FROM AUTHOR]

In three-dimensional video system, the texture and depth videos are jointly encoded, and then the Depth Image Based Rendering (DIBR) is utilized to realize view synthesis. However, the compression distortion of texture and depth videos, as well as the disocclusion problem in DIBR degrade the visual quality of the synthesized view. To address this problem, a Two-stream Attention Network (TSAN)-based synthesized view quality enhancement method is proposed for 3D-High Efficiency Video Coding (3D-HEVC) in this article. First, the shortcomings of the view synthesis technique and traditional convolutional neural networks are analyzed. Then, based on these analyses, a TSAN with two information extraction streams is proposed for enhancing the quality of the synthesized view, in which the global information extraction stream learns the contextual information, and the local information extraction stream extracts the texture information from the rendered image. Third, a Multi-Scale Residual Attention Block (MSRAB) is proposed, which can efficiently detect features in different scales, and adaptively refine features by considering interdependencies among spatial dimensions. Extensive experimental results show that the proposed synthesized view quality enhancement method achieves significantly better performance than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Stereoscopic image retargeting aims at converting stereoscopic images to the target resolution adaptively. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and depth consistency of 3D scenes. In this paper, we present a stereoscopic image retargeting method based on deep convolutional neural network to obtain high-quality retargeted images with both object shape preservation and scene depth preservation. First, a cross-attention extraction mechanism is constructed to generate attention map, which contains the valuable attention features of the left and right images and the common attention features between them. Second, since the disparity map can provide accurate depth information of objects in 3D scenes, a disparity-assisted 3D significance map generation module is utilized to further preserve the valuable depth information of stereoscopic images. Finally, in order to predict the retargeted stereoscopic images accurately, an image consistency loss is developed to preserve the geometric structure of salient objects, and a disparity consistency loss is introduced to eliminate depth distortions. Experimental results demonstrate that the proposed deep convolutional neural network can provide favorable stereoscopic image retargeting results. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) video contains plentiful multi-domain correlations, including spatial, temporal, and inter-view correlations. In this paper, a deep multi-domain prediction method is proposed for 3D video coding. Different from previous methods, our proposed method utilizes not only spatial and temporal correlations but also inter-view correlation to obtain a more accurate prediction, and adopts deep convolutional neural networks to effectively fuse multi-domain references. More specifically, a hierarchical prediction mechanism, which includes a spatial-temporal prediction network and a multi-domain prediction network, is designed to overcome the fusion difficulty of multi-domain reference information. Furthermore, a progressive spatial-temporal prediction network and a multi-scale multi-domain prediction network are designed to obtain the spatial-temporal prediction result and multi-domain prediction result respectively. Experimental results show that the proposed method achieves considerable bitrate saving compared with 3D-HEVC. [ABSTRACT FROM AUTHOR]

Typical high efficiency video coding (HEVC) encoders use rate-distortion optimization (RDO) to select the best coding parameters among numerous candidates to achieve high compression efficiency. However, calculating the rate-distortion cost for each coding parameter entails high computational complexity, which limits the real-time application of these encoders. In this study, time-consuming processes, such as reconstruction and entropy coding, are removed during the calculation of the full RD cost to reduce the complexity of the encoder. Instead, RD costs are estimated for transformed coefficients by using the RD cost model, which is made up of a distortion model based on the distribution features of the transformed coefficients, a residual rate model based on the characteristics of the entropy coding module, and a simple and convenient header rate model. In addition to the full RD cost, typical encoders also adopt fast RD cost, which is composed of the sum of absolute transform differences (SATD) and header bit. To further reduce encoding complexity, three simplified SATD (SSATD) methods based on DCT and DST features are proposed to serve as substitutes for SATD in fast RD cost, thus reducing the computational complexity of SATD calculation. Experimental results show that 35.53%, 14.57%, and 14.37% of coding time can be reduced respectively under AI, RA and LDP configurations with negligible RD performance loss when applying the proposed RD cost model and the SSATD methods to HM16.7. Besides, the proposed methods also can reduce 4%–9% of encoding time and exhibits the same RD performance as state-of-the-art methods. [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]

Video surveillance and its applications have become increasingly ubiquitous in modern daily life. In video surveillance system, video coding as a critical enabling technology determines the effective transmission and storage of surveillance videos. In order to meet the real-time or time-critical transmission requirements of video surveillance systems, the low-delay (LD) configuration of the advanced high efficiency video coding (HEVC) standard is usually used to encode surveillance videos. The coding efficiency of the LD configuration is closely related to the quantization parameter (QP) cascading technique which selects or determines the QPs for encoding. However, the quantization parameter cascading (QPC) technique currently adopted for the LD configuration in HEVC test model (i.e., HM) is not optimized since it has not taken full account of the reference dependency in coding. In this paper, an efficient QPC technique for surveillance video coding, referred to as QPC-SV, is proposed, considering all inter reference frames under the LD configuration. Experimental results demonstrate the efficacy of the proposed QPC-SV. Compared with the default configuration of QPC in the HM, the QPC-SV achieves significant rate-distortion performance gain with average BD-rates of −9.35% and −9.76% for the LDP and LDB configurations, respectively. [ABSTRACT FROM AUTHOR]

Although the in-loop filtering incorporated in High Efficiency Video Coding (HEVC) standard improves the subjective quality of reconstructed pictures and increases the compression efficiency, it still cannot satisfy the demand for higher quality in the rapid growth of video usage. In this paper, we propose recursive residual convolution neural network (RRCNN)-based in-loop filtering to further improve the quality of reconstructed intra frames while reducing the bitrates. Specifically, RRCNN estimates the residual images between the compressed distorted images and original noncompressed ones, and there are shortcut connections that skip a few stacked layers in the structure of RRCNN to ease the training difficulty. By applying the same set of weights recursively, RRCNN achieves excellent performance while utilizing far fewer parameters. For concise in-loop filtering, we train a single model capable of handling various bitrate settings. Different networks for the filtering of luma and chroma components are designed respectively to better learn the filtering characteristics of different channels. Moreover, to fully adapt the various input videos and boost the performance, a coding tree unit (CTU) control flag is signaled to indicate the filtering method from the sense of rate-distortion optimization (RDO). Extensive experimental results show that our scheme achieves significant bitrate savings compared to HEVC, leading to on average 8.7% BD-rate reduction, with up to a 15.1% BD-rate reduction for luma, and more than 20% BD-rate reductions for chroma on average. [ABSTRACT FROM AUTHOR]

Discrete cosine transform (DCT) is an indispensable module in video codecs and is a major part in many video coding standards including the latest high efficiency video coding (HEVC). As the video resolution increases, both transform sizes and the number of transforms increase continuously which poses challenges to the reusability design especially in hardware implementation. This paper presents reconfigurable transform architecture to flexibly support the reusability of different transform sizes. The proposed architecture maximally reuses the hardware resources by rearranging the order of input data for different transform sizes while still exploiting the butterfly property. Furthermore, this architecture supports reconfigurable throughput according to different hardware resource requirements. By applying the proposed architecture to the field-programmable gate array (FPGA) design of HEVC core transform matrices, the synthesis results show much lower consumption of hardware resources comparing to existing methods in the literature. The implementation in Altera’s Stratix III FPGA can operate at 139 MHz and supports real-time processing of $3840\times 2160$ ultra-high definition video at a minimum of 45 f/s and up to 359 f/s for different DCT sizes. [ABSTRACT FROM AUTHOR]

As a significant branch of virtual reality, stereoscopic image stitching aims to generating wide perspectives and natural-looking scenes. Existing 2D image stitching methods cannot be successfully applied to the stereoscopic images without considering the disparity consistency of stereoscopic images. To address this issue, this paper presents a stereoscopic image stitching method based on disparity-constrained warping and blending, which could avoid visual distortion and preserve disparity consistency. First, a point-line-driven homography based disparity minimization method is designed to pre-align the left and right images and reduce vertical disparity. Afterwards, a multi-constraint warping is proposed to further align the left and right images, where the initial disparity map is introduced to control the consistency of disparities. Finally, a disparity consistency seam-cutting and blending method is presented to determine the optimal seam and conduct stereoscopic image stitching. Experimental results demonstrate that the proposed method achieves competitive performance compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Co-saliency detection aims to discover common and salient objects in an image group containing more than two relevant images. Moreover, depth information has been demonstrated to be effective for many computer vision tasks. In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement. With the assistance of the intrasaliency map, the inter-image correspondence is formulated as a hierarchical sparsity reconstruction framework. The global sparsity reconstruction model with a ranking scheme focuses on capturing the global characteristics among the whole image group through a common foreground dictionary. The pairwise sparsity reconstruction model aims to explore the corresponding relationship between pairwise images through a set of pairwise dictionaries. In order to improve the intra-image smoothness and inter-image consistency, an energy function refinement model is proposed, which includes the unary data term, spatial smooth term, and holistic consistency term. Experiments on two RGBD co-saliency detection benchmarks demonstrate that the proposed method outperforms the state-of-the-art algorithms both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

In image and video compression for many multimedia applications, an image/frame is divided into component blocks or patches and is then encoded using some type of transform. Traditional transforms use a complete dictionary of basis functions. A recent technique of growing interest is signal approximation using a linear combination of basis functions from an overcomplete dictionary. The result is a sparse set of coefficients that can represent the original signal and is called sparse coding. This is an NP-hard problem. Orthogonal matching pursuit is a greedy algorithm that is effectively used to address this problem. Keeping in mind the iterative nature of this algorithm, in a recent conference publication, we proposed a rate distortion optimization (RDO) method to select the best sparse representation among iterations up to a target sparsity level. In this paper, we expand the work and consider an adaptive coding scheme that takes advantage of both discrete cosine transform (DCT) and sparse coding. This scheme shows a better performance over plain DCT or sparse coding schemes. We further propose a scheme to increase the coding efficiency of sparse coding by quantizing the sparse coefficients. We investigate an RDO method to select the value of the quantization parameter from a range, balancing distortion, and bit rate. Based on experimental results, we provide a comparison between conventional DCT-based coding, sparse coding scheme, our mixed coding scheme, and the proposed method that includes quantization of the sparse coefficients. [ABSTRACT FROM AUTHOR]

Perceptual image quality assessment (IQA) plays an important role in numerous applications, including image restoration, compression, enhancement, and others. Although many works have been conducted on individually distorted IQA problems and have achieved encouraging results, few studies have been conducted on multiple distorted (MD) IQA problems. Thus, limited progress has been made. In this paper, we propose a novel no reference image quality assessment (NR-IQA) method, named improved multiscale local binary pattern (IMLBP), for addressing multiply distorted IQA problems. The image structures are sensitive to image distortions, which motivates us to utilize the structural characteristics for overall image quality prediction. We improved the local binary pattern (LBP) by considering the human visual mechanism to better extract the structural information. The IMLBP contains two parts, the LBP and the radius difference LBP (DLBP). The DLBP reflects the values’ changes in the radial direction. Specifically, when the radius value is small, the proposed descriptor is computed to represent microstructural information. Conversely, it represents macrostructural information when the radius becomes large. Moreover, to better mimick the human visual mechanism, the IMLBP is computed with the multiscale strategy and the operation is based on a patch unit whose size is proportional to the radius value. The frequency histogram of feature maps is transformed to feature vectors. Subsequently, a predictable function trained by the support vector regression is used to infer the overall quality score. Experimental results show that the proposed method outperforms most state-of-the-art IQA metrics on publicly available multiply distorted image databases. [ABSTRACT FROM AUTHOR]

In this paper, a novel rate-control algorithm based on the region adaptive R- $\lambda $ model is proposed for depth maps coding. First, in order to obtain an accurate rate control for depth maps coding, a modified frame level bit allocation method based on coding bits statistical distribution of depth maps is proposed. Second, considering that different areas in a depth map have an imparity effect on virtual view rendering, the blocks of the depth map are divided into two types, namely, interested blocks for virtual view rending (IBV) and noninterested blocks for virtual view rending (NIBV). Then, two different R- $\lambda $ models are derived for IBV and NIBV, respectively. The optimal bitrates for IBV and NIBV are determined by solving an optimization problem. After that, based on the regional R- $\lambda $ models, the optimal Lagrange multipliers are calculated for both IBV and NIBV. Finally, the largest coding unit (LCU) level rate control is performed by adaptively adjusting the Lagrange multiplier to avoid blocking artifacts and smooth the quality of coding. Experimental results demonstrate that the proposed method can achieve considerable BD-PSNR gains compared with the unified rate-quantization model and conventional R- $\lambda $ model-based algorithms in terms of rendered virtual views quality. [ABSTRACT FROM AUTHOR]

In this paper, we establish a mathematical model that relates the control states and the saliency values in salient object detection. We show that a linear feedback control system (LFCS) is amenable to saliency detection tasks owing to its functional properties. This inspired us to employ an LFCS to detect salient objects in static images. Based on the novel iteration method, the system gradually converges to an optimized stable state, which is associated with an accurate saliency map. In addition, to initialize the system, we propose a so-called boundary homogeneity based on a priori knowledge of the boundary in order to estimate the background likelihood and indirectly obtain a foreground (saliency) map. The experimental results indicate that such a feedback control model can offer significant improvement in salient object detection performance. [ABSTRACT FROM PUBLISHER]