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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]

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]

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]

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]