Texture Video Coding Research Articles

A Bit Allocation Method Based on Inter-View Dependency and Spatio-Temporal Correlation for Multi-View Texture Video Coding

Due to the limited storage capacity and network bandwidth, an efficient rate control (RC) algorithm becomes more and more critical for multi-view video coding (MVC). Based on inter-view dependency and spatio-temporal correlation, a novel bit allocation method for multi-view texture video coding is proposed in this article. Firstly, considering that the distortion in the base view (BV) is directly transmitted to the dependent view (DV) by inter-view skip mode, a joint multi-view RD model is built based on the inter-view dependency. Based on the proposed joint multi-view RD model, a precise power model is derived to represent the target bitrates relationship between the BV and the DV. Secondly, since the P frame in the DV (P-DV) is mainly predicted from the corresponding I frame in the BV (I-BV) by disparity compensated prediction (DCP), the constant proportional relationship between the ratio of the average bitrates of the P-DV to the corresponding I-BV and the ratio of the total bitrates of the DV to the BV is discovered. Based on this discovery, a novel linear model is developed to assign the target bitrates of the P-DV. Finally, considering the spatio-temporal correlation, a new parameter prediction method is proposed for the R-$\lambda $ model in coding tree unit (CTU) level. Extensive experimental results show that the proposed overall method outperforms other state-of-the-art algorithms in terms of RD performance.

Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

Three-dimensional extension of the high efficiency video coding (3D-HEVC) is an emerging international video compression standard for multiview video system applications. Similar to HEVC, a computationally expensive mode decision is performed using all depth levels and prediction modes to select the least rate-distortion (RD) cost for each coding unit (CU). In addition, new tools and intercomponent prediction techniques have been introduced to 3D-HEVC for improving the compression efficiency of the multiview texture videos. These techniques, despite achieving the highest texture video coding efficiency, involve extremely high-complex procedures, thus limiting 3D-HEVC encoders in practical applications. In this paper, a fast texture video coding method based on motion homogeneity is proposed to reduce 3D-HEVC computational complexity. Because the multiview texture videos instantly represent the same scene at the same time (considering that the optimal CU depth level and prediction modes are highly multiview content dependent), it is not efficient to use all depth levels and prediction modes in 3D-HEVC. The motion homogeneity model of a CU is first studied according to the motion vectors and prediction modes from the corresponding CUs. Based on this model, we present three efficient texture video coding approaches, such as the fast depth level range determination, early SKIP/Merge mode decision, and adaptive motion search range adjustment. Experimental results demonstrate that the proposed overall method can save 56.6% encoding time with only trivial coding efficiency degradation.

Region-based rate control for 3D-HEVC based texture video coding

Bit starvation resulted from inefficient rate control of 3D video coding deteriorates visual quality of synthesized views. Most region based rate control schemes allocate higher bitrate to regions of interest (ROIs), but bits may be still consumed by early coded units. This paper avoids reducing coding bits in high-cost regions of 3D-HEVC. Instead of ROIs that are determined by humans, high-cost regions sensitive to bit starvation are detected. Region-level bit allocation is achieved by curve fitting of coding statistics, and recursive Taylor expansion (RTE) based bit allocation is adopted to optimally estimates the target bitrate and the QP related λ of LCUs in each region. Based on retained earnings in economics, bits are reserved for LCUs that are sensitive to distortions. Experimental results show that the proposed scheme outperforms both the R-λ model and RTE model in bitrate accuracy with similar R-D performance.

Low Complexity Texture Video Coding for Multi-View Video System Reliability

Low Complexity Texture Video Coding for Multi-View Video System Reliability

Multiview-Video-Plus-Depth Coding Based on the Advanced Video Coding Standard

This paper presents a multiview-video-plus-depth coding scheme, which is compatible with the advanced video coding (H.264/AVC) standard and its multiview video coding (MVC) extension. This scheme introduces several encoding and in-loop coding tools for depth and texture video coding, such as depth-based texture motion vector prediction, depth-range-based weighted prediction, joint inter-view depth filtering, and gradual view refresh. The presented coding scheme is submitted to the 3D video coding (3DV) call for proposals (CfP) of the Moving Picture Experts Group standardization committee. When measured with commonly used objective metrics against the MVC anchor, the proposed scheme provides an average bitrate reduction of 26% and 35% for the 3DV CfP test scenarios with two and three views, respectively. The observed bitrate reduction is similar according to an analysis of the results obtained for the subjective tests on the 3DV CfP submissions.

Compact Representation for Dynamic Texture Video Coding Using Tensor Method

Dynamic textures are important parts of natural video signals that usually generate an enormous size of high-dimensional data; therefore, effective representation methods are needed for relevant applications. This paper presents a new method for compact representation of high-dimensional data based on tensor decomposition, which can preserve the native form of the data. By treating the high-dimensional data as higher-order tensors, we propose a multiple tensor rank-R decomposition (MTRD) algorithm, which uses low-rank tensors to iteratively approximate the original tensor. Through our MTRD algorithm, the dimension of the data can be greatly reduced, and the decomposition coefficients give a compact representation of the data. As our compact representation method can characterize regular textures in video well, we apply it to dynamic texture video coding, and achieve a better video quality than H.264/AVC with a very low bit-rate just by simply quantizing and coding the decomposition coefficients. Experimental results show that the peak signal-to-noise ratio values of the reconstructed testing sequences can be improved from approximately 0.28 dB up to 8.96 dB, while their bit-rate reductions range from approximately 1.34% to 64.92%.