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We propose a novel two-stage training strategy with ambiguity boosting for the self-supervised learning of single view depths from stereo images. Our proposed two-stage learning strategy first aims to obtain a coarse depth prior by training an auto-encoder network for a stereoscopic view synthesis task. This prior knowledge is then boosted and used to self-supervise the model in the second stage of training in our novel ambiguity boosting loss. Our ambiguity boosting loss is a confidence-guided type of data augmentation loss that improves the accuracy and consistency of generated depth maps under several transformations of the single-image input. To show the benefits of the proposed two-stage training strategy with boosting, our two previous depth estimation (DE) networks, one with t-shaped adaptive kernels and the other with exponential disparity volumes, are extended with our new learning strategy, referred to as DBoosterNet-t and DBoosterNet-e, respectively. Our self-supervised DBoosterNets are competitive, and in some cases even better, compared to the most recent supervised SOTA methods, and are remarkably superior to the previous self-supervised methods for monocular DE on the challenging KITTI dataset. We present intensive experimental results, showing the efficacy of our method for the self-supervised monocular DE task. [ABSTRACT FROM AUTHOR]

With multi-angle video contents available at a user terminal, the user can select his/her preferred alternate views among the given multiple video streams captured at different view angles for a same event. This enhanced experience often entails streaming problems in real-time over the network. In order to cope with this problem, multi-angle video contents are encoded at different bit rates and the appropriate video streams are then selected or transcoded for delivery to meet such bandwidth constraints. Therefore, the selection and transcoding operations become essential processing steps to adapt to the time-varying bandwidth of the network. In this paper, we propose an optimal adaptation framework for streaming multiple video streams by jointly formulating the selection and transcoding problems into a unified optimization problem. Index Terms--Adaptation, multi-angle video, optimization problem, separable programming.

Linear and nonlinear adaptive networks are used for target discrimination in synthetic aperture radar imagery. Problems associated with the two-parameter constant false alarm rate detector led to the use of the quadratic gamma discriminator (QGD) as a classifier. This classifier is further improved in terms of linear processing as a multilayer perceptron (MLP) topology called the nonlinear QGD (NL-QGD).

Recently, due to the strength of deep convolutional neural networks (CNN), many CNN-based image quality assessment (IQA) models have been studied. However, previous CNN-based IQA models likely have yet to utilize the characteristics of the human visual system (HVS) fully for IQA problems when they simply entrust everything to the CNN, expecting it to learn from a training dataset. Therefore, the performance capabilities of such deep-learning-based methods are somewhat saturated. However, in this article, we propose a novel saliency-channel attention residual network based on the just-noticeable-difference (JND) concept for full-reference image quality assessments (FR-IQA). It is referred to as JND-SalCAR and shows significant improvements in large IQA datasets with various types of distortion. The proposed JND-SalCAR effectively learns how to incorporate human psychophysical characteristics, such as visual saliency and JND, into image quality predictions. In the proposed network, a SalCAR block is devised so that perceptually important features can be extracted with the help of saliency-based spatial attention and channel attention schemes. In addition, a saliency map serves as a guideline for predicting a patch weight map in order to afford stable training of end-to-end optimization for the JND-SalCAR. To the best of our knowledge, our work presents the first HVS-inspired trainable FR-IQA network that considers both visual saliency and the JND characteristics of the HVS. When the visual saliency map and the JND probability map are explicitly given as priors, they can be usefully combined to predict IQA scores rated by humans more precisely, eventually leading to performance improvements and faster convergence. The experimental results show that the proposed JND-SalCAR significantly outperforms all recent state-of-the-art FR-IQA methods on large IQA datasets in terms of the Spearman rank order coefficient (SRCC) and the Pearson linear correlation coefficient (PLCC). [ABSTRACT FROM AUTHOR]

In this paper, we propose a new R/D estimation method for intra-predictive coding with deep block partitioning structures. In our proposed R/D prediction, we adopt a particle filtering based prediction (PFP) to precisely predict intermediate R/D estimates for the next frame in a stochastic manner, which helps increasing the prediction accuracy of fast changing R/D values. Then, based on the intermediate R/D estimates by PFP, we infer an optimal model parameter of the TC’s probability density function (pdf) via convex optimization. We found that the proposed method brings about more stable R/D estimation performance thanks to both the improved prediction accuracy using the PFP for abrupt changes in true R/D values and the precise estimation of the optimal model parameter. Experimental results show that our method significantly reduces the normalized root mean square error from average 3.17 to 0.79 (74.90% reduction) for rate and from average 2.32 to 0.82 (64.61% reduction) for distortion, compared to the state-of-the art method. [ABSTRACT FROM AUTHOR]

Recently, deep convolutional neural networks (CNNs) have started to be applied for automatic target recognition (ATR) problems of synthetic aperture radar (SAR) images. In conventional SAR-ATR algorithms, the pose angle information of the target has been importantly used. However, recent deep learning-based SAR-ATR algorithms often only utilize the intensity information. In this paper, based on the prior works that the pose angle is an important latent variable for boosting target recognition performance, we propose a CNN-based SAR target recognition network with pose angle marginalization learning, called SPAM-Net that marginalizes the conditional probabilities of SAR targets over their pose angles to precisely estimate the true class probabilities. The proposed SPAM-Net consists of two sub-nets: (i) a sub-net for class-conditional probability estimation, called CP sub-net, and (ii) a sub-net for pose angle probability estimation, called PP sub-net. The two sub-nets are jointly learned via an end-to-end manner in a Bayesian framework so that the SPAM-Net incorporates the pose angle information into target recognition task effectively. The SPAM-Net outperforms our baseline network that does not utilize the pose angle information. In the experiments, we intensively analyze the effectiveness of pose angle information for SAR-ATR, revealing that more accurate pose angle information helps the SPAM-Net precisely estimate target classes for the misclassified target groups that are obtained by the baseline network. Furthermore, our method also outperforms the other state-of-the-art SAR-ATR algorithms, yielding the correct target recognition rate with average 99.61%. [ABSTRACT FROM AUTHOR]

Camera pose estimation from structured array images is critical for systematically reconstructing depth information of omnidirectional object scenes and fully understanding the given situation of a measuring camera system from captured images. The fourth paper entitled "Voxel-wise UV parameterization and view-dependent texture synthesis for the immersive rendering of TSDF Scene model" by Soowoong Kim and others introduces a voxel-wise UV parameterization method that delegates a precomputed UV map to each voxel based on the UV map look-up table. Beyond the conventional media contents of planar displays, realistic and immersive media technologies have been researched actively for decades as one of the crucial technologies in the fourth industrial revolution. [Extracted from the article]

Recently, many deep-learning-based pan-sharpening methods have been proposed for generating high-quality pan-sharpened (PS) satellite images. These methods focused on various types of convolutional neural network (CNN) structures, which were trained by simply minimizing a spectral loss between network outputs and the corresponding high-resolution (HR) multi-spectral (MS) target images. However, owing to different sensor characteristics and acquisition times, HR panchromatic (PAN) and low-resolution MS image pairs tend to have large pixel misalignments, especially for moving objects in the images. Conventional CNNs trained with only the spectral loss with these satellite image data sets often produce PS images of low visual quality including double-edge artifacts along strong edges and ghosting artifacts on moving objects. In this letter, we propose a novel loss function, called a spectral-spatial structure (S3) loss, based on the correlation maps between MS targets and PAN inputs. Our proposed S3 loss can be very effectively used for pan-sharpening with various types of CNN structures, resulting in significant visual improvements on PS images with suppressed artifacts. [ABSTRACT FROM AUTHOR]

Although high dynamic range (HDR) display has become popular recently, the legacy content such as standard dynamic range (SDR) video is still in service and needs to be properly converted on HDR display devices. Therefore, it is desirable for HDR TV sets to have the capability of automatically converting input SDR video into HDR video, which is called reverse tone mapping (RTM). In this paper, we propose a novel learning-based low-complexity RTM scheme that not only expands the suppressed dynamic ranges (DR) of the SDR videos (or images), but also effectively restores lost detail in the SDR videos. Most existing conventional RTM schemes have focused on how to expand the DR of global contrast, resulting in limitations in recovering lost detail of SDR videos. On the other hand, the recent convolutional neural network-based approaches show promising results, but they are too complex to be applied on the users’ devices in practice. In this paper, our learning-based RTM scheme is computationally simple but effective in recovering lost detail. To learn the SDR-to-HDR relation, training “SDR-HDR” images are first separated into their base layer components and detail layer components by applying a guided filter. The detail layer components of the “SDR-HDR” pairs are used to train the SDR-to-HDR mapping. The mapping matrices are computed based on kernel ridge regression. In the meantime, the global contrast of the base layers is expanded by a nonlinear function that suppresses darker regions and amplifies brighter regions to fit the full DR of a target HDR display. To verify the effectiveness of our learning-based RTM scheme, we performed subjective quality assessment for images and videos. The experimental results show that our RTM scheme outperforms the existing RTM scheme with the successful restoration of lost detail in SDR images. [ABSTRACT FROM AUTHOR]

Joint exploration model (JEM) reference codecs of ISO/IEC and ITU-T utilize multiple types of integer transforms based on DCT and DST of various transform sizes for intra- and inter-predictive coding, which has brought a significant improvement in coding efficiency. JEM adopts three types of integer DCTs (DCT-II, DCT-V, and DCT-VIII), and two types of integer DSTs (DST-I and DST-VII). The fast computations of Integer DCT-II and DST-I are well known, but few studies have been performed for the other types such as DCT-V, DCT-VIII, and DST-VII for all transform sizes. In this paper, we present fast computation methods of N-point DCT-V and DCT-VIII. For this, we first decompose the DCT-VIII into a preprocessing matrix, the DST-VII and a post-processing matrix to quickly compute it by using the linear relation between DCT-VIII and DST-VII. Then, we approximate integer kernels of N = 4, 8, 16, and 32 for DCT-V, DCT-VIII, and DST-VII with norm scaling and bit-shift to be compatible with quantization in each stage of multiplications between decomposed matrices for video coding. In various experiments, the proposed fast computation methods have shown to effectively reduce the total complexity of the matrix operations with little loss in BDBR performance. In particular, our methods reduce the number of addition and multiplication operations by 38% and 80.3%, respectively, in average, compared to the original JEM. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel hardware-friendly super-resolution (SR) method based on a convolutional neural network (CNN) and its dedicated hardware (HW) on field programmable gate array (FPGA). Although CNN-based SR methods have shown very promising results for SR, their computational complexities are prohibitive for hardware implementation. To the best of our knowledge, we are the first to implement a real-time CNN-based SR HW that upscales 2K full high-definition video to 4K ultra high-definition (UHD) video at 60 frames per second (fps). In our dedicated CNN-based SR HW, low-resolution input frames are processed line-by-line, and the number of convolutional filter parameters is reduced significantly by incorporating depth-wise separable convolutions with a residual connection. Our CNN-based SR HW incorporates a cascade of 1D convolutions having large receptive fields along horizontal lines while keeping vertical receptive fields minimal, which allows us to save required line memory space in achieving comparable SR performance against full 2D convolution operations. For efficient HW implementation, we use a simple and effective quantization method with little peak signal-to-noise ratio (PSNR) degradation. Also, we propose a compression method to efficiently store intermediate feature map data to reduce the number of line memories used in HW. Our HW implementation on the FPGA generates 4K UHD frames of higher PSNR values at 60 fps and shows better visual quality, compared with conventional CNN-based SR methods that are trained and tested in software. [ABSTRACT FROM AUTHOR]

In the latent Dirichlet allocation (LDA) model, each image is represented by word distributions with their latent topics. Since the previous LDA-based models are not capable of dealing with the spatial information of visual words in images, this paper focuses on discovering the latent topics of images with visual saliency. To accomplish this, a saliency-weighted LDA (swLDA) model is proposed that incorporates visual saliency into the topic distribution of visual words, in a manner similar to human perception. The topic distributions of the visual words were learned with saliency weights reflecting whether the visual words were in the salient or nonsalient regions. The experimental results demonstrate that the swLDA model effectively incorporates visual saliency as a focus of attention, mimicking human perception behavior, remarkably outperforming previous LDA models in terms of image categorization. [ABSTRACT FROM AUTHOR]

We present a novel and effective learning-based frame rate upconversion (FRUC) scheme, using linear mapping. The proposed learning-based FRUC scheme consists of: 1) a new hierarchical extended bilateral motion estimation (HEBME) method; 2) a light-weight motion deblur (LWMD) method; and 3) a synthesis-based motion-compensated frame interpolation (S-MCFI) method. First, the HEBME method considerably enhances the accuracy of the motion estimation (ME), which can lead to a significant improvement of the FRUC performance. The proposed HEBME method consists of two ME pyramids with a three-layered hierarchy, where the motion vectors (MVs) are searched in a coarse-to-fine manner via each pyramid. The found MVs are further refined in an enhanced resolution of four times by jointly combining the MVs from the two pyramids. The HEBME method employs a new elaborate matching criterion for precise ME which effectively combines a bilateral absolute difference, an edge variance, pixel variances, and an MV difference among two consecutive blocks and its neighboring blocks. Second, the LWMD method uses the MVs found by the HEBME method and removes the small motion blurs in original frames via transformations by linear mapping. Third, the S-MCFI method finally generates interpolated frames by applying linear mapping kernels for the deblurred original frames. In consequence, our FRUC scheme is capable of precisely generating interpolated frames based on the HEBME for accurate ME, the S-MCFI for elaborate frame interpolation, and the LWMD for contrast enhancement. The experimental results show that our FRUC significantly outperforms the state-of-the-art non-deep learning-based schemes with an average of 1.42 dB higher in the peak signal-to-noise-ratio and shows comparable performance with the state-of-the-art deep learning-based scheme. [ABSTRACT FROM AUTHOR]

Based on our previous super-interpolation method, we propose a novel hardware-friendly super-resolution (SR) algorithm, called HSI method, and its dedicated hardware architecture for up-scaling full-high-definition (FHD) video streams to 4K ultra-high-definition (UHD) video streams in real-time. Our proposed HSI method involves training and up-scaling steps. In the training step, an edge-orientation-based clustering is applied for low-resolution (LR) training patches to obtain a training patch set for each class, and a linear mapping kernel is learned from LR to high-resolution (HR) based on the training patch set for each class. In the up-scaling step, each LR input patch is transformed to an HR patch by applying the linear mapping kernel for its class. We implemented the up-scaling step of our HSI method by a dedicated hardware (HW) with the pre-trained linear mapping kernels stored in a look-up table. Our HW implementation, called HSI HW, contains 159K gate counts and achieves about 880 Mpixels/s throughput by using the TSMC 0.13-um CMOS process, and thus performing the SR operation from FHD to 4K UHD in real-time. Compared with conventional SR methods, our HW implementation of HSI reconstructs HR images of higher peak signal to noise ratio values and better visual quality. [ABSTRACT FROM AUTHOR]

The new MPEG-4 video coding standard enables content-based functionalities. In order to support the philosophy of the MPEG-4 visual standard, each frame of video sequences should be represented in terms of video object planes (VOP's). In other words, video objects to be encoded in still pictures or video sequences should be prepared before the encoding process starts. Therefore, it requires a prior decomposition of sequences into VOP's so that each VOP represents a moving object. This paper addresses an image segmentation method for separating moving objects from the background in image sequences. The proposed method utilizes the following spatio-temporal information. 1) For localization of moving objects in the image sequence, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed by comparing two variance estimates from two consecutive difference images, which results in an F-test. 2) Spatial segmentation is performed to divide each image into semantic regions and to find precise object boundaries of the moving objects. The temporal segmentation yields a change detection mask that indicates moving areas (foreground) and nonmoving areas (background), and spatial segmentation produces spatial segmentation masks. A combination of the spatial and temporal segmentation masks produces VOP's faithfully. This paper presents various experimental results. Index Terms-- Object segmentation, change detection mask, temporal segmentation, spatial segmentation, and tracking.

Conventional predictive video coding-based approaches are reaching the limit of their potential coding efficiency improvements, because of severely increasing computation complexity. As an alternative approach, perceptual video coding (PVC) has attempted to achieve high coding efficiency by eliminating perceptual redundancy, using just-noticeable-distortion (JND) directed PVC. The previous JNDs were modeled by adding white Gaussian noise or specific signal patterns into the original images, which were not appropriate in finding JND thresholds due to distortion with energy reduction. In this paper, we present a novel discrete cosine transform-based energy-reduced JND model, called ERJND, that is more suitable for JND-based PVC schemes. Then, the proposed ERJND model is extended to two learning-based just-noticeable-quantization-distortion (JNQD) models as preprocessing that can be applied for perceptual video coding. The two JNQD models can automatically adjust JND levels based on given quantization step sizes. One of the two JNQD models, called LR-JNQD, is based on linear regression and determines the model parameter for JNQD based on extracted handcraft features. The other JNQD model is based on a convolution neural network (CNN), called CNN-JNQD. To our best knowledge, our paper is the first approach to automatically adjust JND levels according to quantization step sizes for preprocessing the input to video encoders. In experiments, both the LR-JNQD and CNN-JNQD models were applied to high efficiency video coding (HEVC) and yielded maximum (average) bitrate reductions of 38.51% (10.38%) and 67.88% (24.91%), respectively, with little subjective video quality degradation, compared with the input without preprocessing applied. [ABSTRACT FROM AUTHOR]

In this paper, an all-zero block detection scheme is proposed prior to DCT to reduce the encoding complexity for high efficiency video coding (HEVC). Since many coding blocks tend to have all zero coefficients after DCT and quantization, it is worthwhile to detect all-zero-quantized blocks for input residual blocks before DCT so that subsequent transform and quantization can be skipped. Unlike previous coding standards, HEVC adopts large transform sizes such as $16 \times 16$ and $32 \times 32$ in addition to $4 \times 4$ and $8 \times 8$. It becomes more difficult to accurately detect all-zero blocks in HEVC because the large transform blocks contains more variety of content characteristics than smaller ones, thus making it ineffective the existing all-zero block (AZB) detection schemes for large transform blocks in HEVC. In this paper, a novel AZB detection scheme is proposed for the case that Hadamard transform is used as a distortion metric for RDO in HEVC. Statistical upper bounds to be all-zero blocks are derived using the relationship between Walsh Hadamard and DCT transform kernels. Then, a small number of quantized coefficients in a upper left corner of a transform block, which are obtained using the relations between Hadamard transform and DCT, are examined for AZB detection. For $32 \times 32$ blocks, DC coefficients of $8 \times 8$ sub-blocks are further examined for AZB detection. The experimental results demonstrate that the proposed scheme detects 87.79% of actual AZBs with 2.87% false alarm rate in average, outperforming the state-of-the-art method. Computational complexity to detect AZB is almost negligible compared to the conventional method. [ABSTRACT FROM PUBLISHER]

In this paper, a High Efficiency Video Coding (HEVC)-compliant perceptual video coding (PVC) scheme is introduced based on just-noticeable difference (JND) models in both transform and pixel domains. We adopt an existing pixel-domain JND model for the transform skip mode of HEVC and propose a transform-domain JND model for the transform nonskip modes of HEVC. The proposed transform-domain JND model is designed by considering the spatial JND characteristics such as contrast sensitivity, luminance adaptation, and contrast masking effects as well as by considering the summation effects of variable block-sized transforms in HEVC. A temporal JND model is additionally incorporated into the proposed transform-domain JND model to further reduce perceptual redundancy. To incorporate the transform- and pixel-domain JND models into the encoding process in an HEVC-compliant manner, the transform coefficients and residues are suppressed in harmonization with the transform/quantization process and the quantization-only process of HEVC, respectively. To make the JND-based suppression effective, a distortion compensation factor is also proposed to reflect the perceptual distortion in the rate-distortion optimization-based encoding process. Based on subjective quality assessments of the encoded bit streams of test sequences, the proposed HEVC-compliant PVC scheme yields remarkable bitrate reductions of a maximum 49.10% and an average 16.10% with negligible subjective quality loss, compared with an HEVC reference software HEVC test model (HM 11.0). In addition, the proposed HEVC-compliant PVC scheme increases the encoding complexity of HM 11.0 only by an average of 11.25%. [ABSTRACT FROM AUTHOR]

Social TV is a social media service via TV and social networks through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned: grouping of similar TV users to create social TV communities and recommending TV programs based on group and personal interests for personalizing TV. In this paper, we propose a unified topic model based on grouping of similar TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two latent Dirichlet allocation (LDA) models. One is a topic model of TV users, and the other is a topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs, which enforces the grouping of similar TV users and associated description words for watched TV programs at the same time in a unified topic modeling framework. The unified model identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with similar tastes can be grouped as topics, which can then be recommended as social TV communities. To verify our proposed method of unified topic-modeling-based TV user grouping and TV program recommendation for social TV services, in our experiments, we used real TV viewing history data and electronic program guide data from a seven-month period collected by a TV poll agency. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation and its performance is an average of 6.5% higher than that of the topic model of TV users only. For TV user prediction with new TV programs, the average prediction precision was 79.6%. Also, we showed the superiority of our proposed model in terms of both topic modeling performance and recommendation performance compared to two related topic models such as polylingual topic model and bilingual topic model. [ABSTRACT FROM PUBLISHER]

HEVC is a next generation video coding standard designed with modern coding techniques to be especially efficient for coding high-resolution video such as 4 K/8 K-ultra high- definition (UHD) video. Among the advanced coding tools of HEVC, tiles and wavefront parallel processing (WPP) have been newly adopted for parallel processing of such high-resolution (4 K/8 K-UHD) video. To realize UHD video services over portable devices with limited battery power, it is essential to implement multi-core-based and dedicated HEVC hardware decoders that support the tile- and wavefront-based parallel processing. By doing so, each frame is divided into a multiple number of picture partitions which can then be processed by multiple hardware decoder cores in parallel. However, in-loop filtering (ILF) at tile boundaries cannot be easily parallelized by a multi-core HEVC hardware decoder because of the data dependency between samples in different tiles. In this paper, an efficient control method for ILF across tile boundaries is proposed for multi-core HEVC hardware decoders. The proposed method does not require additional in-loop filters for ILF across the tile boundaries and it allows a decoder core to continue to process the next coding tree unit (CTU) without waiting for other decoders until they finish their ILF processing for the neighboring CTUs in other tiles. From experiments, we show the effectiveness of our ILF control method via a quad-core HEVC decoder for 4 K-UHD video implemented on a prototyping FPGA board. [ABSTRACT FROM PUBLISHER]

Recently, a new video coding standard, High Efficiency Video Coding (HEVC), has shown greatly improved coding efficiency by adopting hierarchical structures of coding unit (CU), prediction unit (PU), and transform unit (TU). To best achieve the coding efficiency, the best combinations of CU, PU, and TU must be found in the sense of the minimum rate-distortion (R-D) costs. Owing to this, a large computational complexity occurs. Among these CU, PU, and TU, the determination of CU sizes most significantly affects the R-D performance of HEVC encoders, which causes large computational costs in operation with PU and TU size determinations. In spite of recent works in the complexity reduction of HEVC encoders, most of the research has focused on the complexity reduction with fast CU split in intra slice coding and with early TU split in both intra and inter slice. In this paper, we propose a fast and an efficient CU encoding scheme based on the spatiotemporal encoding parameters of HEVC encoders, which consists of an improved early CU SKIP detection method and a fast CU split decision method. For the current CU block under encoding, the proposed scheme utilizes sample-adaptive-offset parameters as the spatial encoding parameter to estimate the texture complexity that affects the CU partition. In addition, the motion vectors, TU size, and coded block flag information are used as the temporal encoding parameters to estimate the temporal complexity that also affects the CU partition. The proposed scheme effectively utilizes the spatiotemporal encoding parameters that are the byproducts during the encoding process of HEVC without additionally required computation. The proposed novel fast CU encoding scheme significantly reduces the total encoding time with negligible RD-performance loss. The experimental results show that the proposed scheme achieves the total encoding time savings of average 49.6% and 42.7% only with average 1.4% and 1.0% bit-rate losses for various test sequences under random access and low delay B conditions, respectively. The proposed scheme has an advantage on the implementation for parallel processing in pipeline structures of HEVC encoders due to its independency with neighboring CU blocks. [ABSTRACT FROM PUBLISHER]

Asymmetric video coding based on binocular suppression provides a prospect for improving coding efficiency since one view in stereoscopic video can be encoded at lower bitrates than the other view without a loss of perceptual video quality. In this paper, we propose a joint rate control scheme for asymmetric stereoscopic video coding in a hybrid video codec system, which supports binocular suppression-based asymmetric stereoscopic video coding in a theoretical basis within an optimization framework. To obtain an optimal solution to quantization steps for joint rate control, we apply the Karush–Kuhn–Tucker (KKT) conditions in minimizing the perceptual distortion of decoded video under the conditions that: 1) the sum of bits generated from two video encoders is constrained within a given bit budget; 2) the quality of a primary view is superior to that of an auxiliary view with an allowable difference between two view qualities; and 3) the distortion of an auxiliary view is less than a given threshold value for high bitrate encoding. As a result, the proposed rate control scheme effectively enhances the perceptual quality based on an optimization framework by taking into account the binocular suppression and also simultaneously controlling the rates of both the left and right views of stereoscopic video under the constraints with a given target bit budget and an allowed quality difference. Experimental results demonstrate that, compared with the independent rate control scheme, the proposed joint rate control scheme with KKT conditions for asymmetric stereoscopic video coding not only demonstrates comparable 3-D perceived visual quality with an overall gain of 0.33 dB, but also attains 2-D visual quality gain by an average of 2.49 dB while accurately satisfying the given constraints in the proposed optimization framework. [ABSTRACT FROM AUTHOR]

In this paper, a frame-level rate control scheme is proposed based on texture and nontexture rate models for High Efficiency Video Coding (HEVC). Due to more complicated coding structures and the adoption of new coding tools, the statistical characteristics of transform residues are significantly different depending on the depth levels of coding units (CUs) from which the residues are obtained. A new texture rate model is constructed for the transform residues, which are categorized into three types of CUs: low-, medium- and high-textured CUs. One single Laplacian probability PDF model is used for each residue category to derive a rate-quantization model. Based on the Laplacian PDF, a simplified rate model for texture bits is derived using entropy. In addition, an analytic rate model for nontexture bits is proposed, which also takes into account the different characteristics of nontexture bits occurring in various depths of CUs in HEVC. The nontexture bitrates are modeled based on the linear relation between the total nontexture data and the dominant nontexture data in each CU category. Based on the proposed rate models for the texture and nontexture bits, accurate rate control can be achieved owing to more precise rate estimation. The experimental results show that the proposed rate control scheme achieves the average PSNR with 0.44 dB higher and the average PSNR standard deviation of 0.32 point lower with the buffer status levels maintained very close to target buffer levels, compared to the conventional methods. Finally, the proposed rate control scheme remarkably outperforms the conventional schemes especially for the sequences of complex texture and large motion. [ABSTRACT FROM PUBLISHER]

Peak signal-to-noise ratio (PSNR) monitoring is an important application for video quality assessment of video systems at the receiver sides where no-reference PSNR estimation is essential. Most of the PSNR estimation methods for H.264/AVC bitstreams ignore or do not consider the effect of deblocking filtering. Instead, they only focus on estimating the mean squared error (MSE) due to quantization. However, the PSNR estimation affected by deblocking filtering cannot be negligible for sequences of large picture resolutions. In this paper, we first present an MSE estimation method on H.264/AVC bitstreams by considering the deblocking filtering effect so that more accurate PSNR estimation can be made. For this, the total MSE between the original and reconstructed frames is separated into two terms for PSNR estimation: one due to quantization error and the other due to the deblocking filtering effect in H.264/AVC. In the proposed PSNR estimation, the contribution of deblocking filtering to the total MSE is quantified by a compensation factor of each encoded picture type between the original and the deblocked frames. Experimental results show that the proposed method effectively reflects the contribution of deblocking filtering to PSNR estimation, thus yielding more accurate PSNR estimates. [ABSTRACT FROM PUBLISHER]

In this study, the performance of hierarchical transform coding is analysed with design of an order‐16 integer transform kernel. The proposed hierarchical transform‐coding structure is constructed with a set of 4 × 4, 8 × 8 and 16 × 16 integer transforms of variable transform block sizes, which takes the advantages of both lower and higher transform kernels by flexibly adapting to varying image characteristics of video sequences with homogeneous and complex regions. The proposed hierarchical transform‐coding structure is implemented as an extension to H.264/advanced video coding joint model. The authors show the effectiveness of the hierarchical variable‐sized block transform scheme by analysing the quantisation effects and the correlation among neighbouring pixels in video sequences of different spatial resolutions. The experimental results show that: (i) the variable‐sized block transform scheme with the hierarchical structure is advantageous to the texture regions with strong local edges and (ii) the higher‐order‐16 integer transform kernel itself is more effective for the homogeneous texture regions, which are often encountered in higher resolution sequences. Therefore these two features can complementarily work in an rate‐distortion (RD) optimised manner for various characteristics of the input signals. [ABSTRACT FROM AUTHOR]

Highlights: [•] First addresses object detection and tracking with graph-based representation in H.264/SVC. [•] The block partitions with motion information are modeled as spatial and spatio-temporal graphs. [•] Graph-based analyses are performed to remove false objects and precisely identify real objects. [•] Interlayer graph mapping reliably detects and tracks object in the higher spatial layer. [•] It reduces the computation complexity of object detection and tracking in the higher spatial layer. [Copyright &y& Elsevier]

High Efficiency Video Coding (HEVC), a new video coding standard currently being established, adopts a quadtree-based Coding Unit (CU) block partitioning structure that is flexible in adapting various texture characteristics of images. However, this causes a dramatic increase in computational complexity compared to previous video coding standards due to the necessity of finding the best CU partitions. In this paper, a fast CU splitting and pruning method is presented for HEVC intra coding, which allows for significant reduction in computational complexity with small degradations in rate-distortion (RD) performance. The proposed fast splitting and pruning method is performed in two complementary steps: 1) early CU split decision and 2) early CU pruning decision. For CU blocks, the early CU splitting and pruning tests are performed at each CU depth level according to a Bayes decision rule method based on low-complexity RD costs and full RD costs, respectively. The statistical parameters for the early CU split and pruning tests are periodically updated on the fly for each CU depth level to cope with varying signal characteristics. Experimental results show that our proposed fast CU splitting and pruning method reduces the computational complexity of the current HM to about 50% in encoding time with only 0.6% increases in BD rate. [ABSTRACT FROM PUBLISHER]

Recently, 3DTV has drawn much attention as a new broadcasting service. In spite of technical advances in 3DTV broadcasting services, there are two barriers that hinder the new services from being launched for terrestrial broadcasting: the lack of available bandwidth for transmission of additional-view video via terrestrial channel and backward compatibility with the legacy 2-D HDTV services. As an alternative, a hybrid stereoscopic video coding scheme is proposed for a stereoscopic 3DTV service, where one-view video is transmitted through the legacy broadcasting systems and the other one is delivered via broadband networks to which TV terminals are connected. In this paper, the proposed hybrid stereoscopic video coding scheme incorporates an MPEG-2 codec for backward compatibility with a legacy 2-D HDTV service and an HEVC codec with inter-view prediction coding as an extension to encode additional-view video sequences with high coding efficiency. The proposed inter-view prediction coding scheme in the extended HEVC incorporates an advanced motion and disparity vector prediction (AMDVP) method for enhanced motion- and disparity-compensated coding. The experimental results demonstrate that the proposed hybrid stereoscopic video coding scheme achieves an average coding efficiency of 38.22% (32.86%) in BD-rate or 1.394 dB (1.235 dB) in BD-PSNR for the out-band (in-band) scenario. [ABSTRACT FROM PUBLISHER]

Video quality estimation is considered a means of monitoring quality of service in broadcasting or IPTV services. In this paper, a no-reference peak signal-to-noise ratio (PSNR) estimation method is first presented for a quadtree-based motion estimation or compensation and transform coding scheme such as HEVC test model (HM), which is expected to be popularly used due to its highly enhanced coding efficiency, in 2-D and 3-D high resolution videos. The proposed no-reference PSNR estimation method is based on a Laplacian mixture distribution, which takes into account the distribution characteristics of residual transform coefficients in different quadtree depths and coding types of coding units (CUs). In order to predict the model parameters of the Laplacian mixture distribution for all zero quantized coefficients case, an exponential regression scheme is employed over quadtree depth levels of CUs. The proposed no-reference PSNR estimation method yields fairly accurate results from 0.970 to 0.983 in correlation and from 0.530 to 0.890 in RMSE between the actual and the estimated PSNR values for HM encoded bitstreams, outperforming single PDF based models. [ABSTRACT FROM PUBLISHER]

Although the temporal scalability coding supported by both H.264/AVC and H.264/SVC provides a good means of adapting bitstream scalability in temporal dimension, it entails heavy computational complexity due to bidirectional motion estimation and compensation for B-pictures in temporal scalability levels. To make it more applicable, this paper introduces a fast inter-prediction mode decision method which is efficiently performed using statistical hypothesis testing. The hypothesis testing is performed on mean and variance of pixels in 16 \times 16 and 8 \times 8 blocks to decide early termination against further processing for their sub-blocks. Fallback check is employed to compromise the tradeoff between the compression efficiency and encoding time saving. The proposed method exhibits effective performance for early termination in the inter-prediction mode decision, thus leading to a significant reduction of the total encoding time up to average 64.8% with small increments in bit amounts and small degradation in visual quality. [ABSTRACT FROM PUBLISHER]

This paper presents a spatio-temporal graph-based method of detecting and tracking moving objects by treating the encoded blocks with non-zero motion vectors and/or non-zero residues as potential parts of objects in H.264/AVC bitstreams. A spatio-temporal graph is constructed by first clustering the encoded blocks of potential object parts into block groups, each of which is defined as an attributed subgraph where the attributes of the vertices represent the positions, motion vectors and residues of the blocks. In order to remove false-positive blocks and to track the real objects, temporal connections between subgraphs in two consecutive frames are constructed and the similarities between subgraphs are computed, which constitutes a spatio-temporal graph. We show the experimental results that the proposed spatio-temporal graph-based representation of potential object blocks enables effective detection for the small-sized objects and the objects with small motion vectors and residues, and allows for reliable tracking of the detected objects even under occlusion. The identification of the detected moving objects is determined as rectangular regions of interest (ROIs) for which the ROI sizes and positions are adaptively adjusted to give the best approximation of the real shapes and positions of the objects. [ABSTRACT FROM PUBLISHER]

Selective protection can exploit dependency coding properties to effectively perform partial protection on scalable video coding (SVC) bitstreams since protecting frames in lower scalability layers affects visual quality of the reconstructed frames in higher scalability layers. In this paper, we propose a selective protection scheme that maximizes protection effects by the minimum number of encoded frames to be protected in the SVC bitstream domain. We first model the SVC dependency coding structure as a directed acyclic graph which is characterized with an estimated visual quality value as the attribute at each node. In addition, a visual quality estimation model is proposed based on the proportions of intra-predicted and inter-predicted MBs, amounts of residues, and estimated visual quality of reference frames. The proposed selective protection scheme traverses the dependency graph to find optimal protection paths that can give the maximum visual quality degradation. Experimental results show that, compared to the existing protection schemes, the proposed selective protection scheme reduces computation complexity in the number of protected frames, the amount of protected data, and the protection time saving. The SVC file format specification supports the carriage of selectively protected bitstreams based on the concept of our selective protection in dependency coding structure of SVC. [ABSTRACT FROM AUTHOR]