Jianhua Zheng

Texture-Aware Depth Prediction in 3D Video Coding

3D video has raised great interest in the last decade and currently a new 3D video coding standard, known as 3D video coding extension of High Efficiency Video Coding (3D-HEVC), has been developed. The standard investigates the coding of multiview video plus depth, which consists of texture videos and depth videos of multiple views. Depth video, as a description of geometry information of a scene, is generally composed of large flat regions separated by sharp edges. The conventional video coding may fail to generate an accurate prediction for units with sharp edges due to its block-based prediction which cannot compensate (minor) boundary changes well. In order to attack the problem, a new texture-aware depth inter-prediction method is proposed, which incorporates pixel-oriented weighting in the bi-prediction process by exploiting motion and structure similarities between texture and depth videos. Furthermore, such pixel-oriented weighting scheme can be extended to the uni-prediction process by considering more prediction blocks with small motion vector displacements. Experimental results demonstrate that the adapted 3D-HEVC codec with the proposed method can achieve better rate-distortion performance compared to the original 3D-HEVC standard codec.

Reducing Wedgelet lookup table size with down-sampling for depth map coding in 3D-HEVC

In 3D-HEVC, a new coding standard of 3D video, Explicit Wedgelet mode within Depth Modelling Modes (DMM) partitions a depth block into two non-rectangular regions using a Wedgelet pattern selected from a Wedgelet lookup table. The Wedgelet lookup table is constructed during both encoder and decoder initialization for each block size ranging from 4×4 to 16×16, while the Wedgelet lookup table consists of a large number of Wedgelet bi-patterns, which involves relatively high computational complexity and may cause cache storing problem. In this paper, we propose a down-sampling method to reduce the number of Wedgelet patterns in the Wedgelet lookup table. The experimental results show our proposed down-sampling method can achieve storage size reduction by 27.8% with negligible 0.03% and 0.05% coding loss in both configurations of Common Test Conditions (CTC) and All Intra (AI), respectively.

Simplified reference pixel selection for constant partition value coding in 3D-HEVC

To accurately represent object edges in depth maps, new depth intra prediction modes, namely Depth Modelling Modes (DMMs), are introduced into 3D-HEVC. In the DMMs, a depth block is approximated by a model that partitions the block into two non-rectangular regions, where each region is represented by a constant value referred to as Constant Partition Value (CPV). To code the CPV, a predicted CPV for each region is obtained by using the adjacent pixels of available neighboring blocks. However, the current reference pixel selection method may fail to find a good prediction in some cases, as to be shown in this paper. Accordingly we propose a simplified yet more effective reference pixel selection method to streamline the CPV prediction. While simplifying the reference selection process in terms of computational complexity, the proposed method also demonstrates 0.07% and 0.06% BD-rate saving for synthesized views, under all-intra case and random-access case, respectively.

Improved segment-wise DC coding for HEVC intra prediction of depth maps

This paper presents an improved Segment-wise Direct Component (DC) Coding (SDC) for High Efficiency Video Coding (HEVC) intra prediction of depth maps. In the current 3D-HEVC, predicted DC in the SDC for HEVC intra prediction modes is calculated as the average of four corner samples in a predicted block. However, this method may introduce errors when SDC employs depth lookup table (DLT). To reduce the DLT-induced error, we propose to utilize the right-bottom sample as the predicted DC in the SDC. The experimental results show that the proposed method can achieve 0.27% and 0.10% BD-rate savings in All-Intra configurations (AI) and common test conditions (CTC) based on 3D-HTM 11.0, respectively.

An Efficient Partition Scheme for Depth-Based Block Partitioning in 3D-HEVC

In the development of a 3D video extension of High Efficiency Video Coding HEVC standard, namely 3D-HEVC, Depth-based Block Partitioning DBBP is employed to code texture videos in dependent views by utilizing coded depth information of an independent view. With the DBBP, a proper partition mode is determined with the coded depth information, which divides the current texture block into two regions and thereafter allows for fine-grained motion compensation of foreground and background separately. In the DBBP, the original partition method consists of two steps specifically, i.e. threshold calculation and matched filtering based on down-sampling, which is relatively high complex and redundant with a segment mask generation process. Accordingly, a simple yet more effective partition scheme for the DBBP coding is proposed in this paper, based on the available binary segment mask. While reducing computational complexity significantly, the proposed method also demonstrates bitrate saving for all the dependent texture views and synthesized views under common test conditions CTC configuration specified in the 3D-HEVC.

3D-Spatial-Texture Bilateral Filter for Depth-Based 3D Video

In the depth-based 3D video system, filters used in texture and depth images denoising, such as bilateral filter and trilateral filter, are generally designed based on calculating the weighted average of reference pixels located around the pixel to be filtered. In this paper, we propose a 3D-spatial-texture bilateral filter by considering the relationship of two pixels in the 3D space, including geometric closeness in the 3D world coordinate, as well as their corresponding texture/color similarity. Accordingly, the weight is defined with two kernels describing two abovementioned factors respectively, namely, a spatial kernel and a range kernel. The experimental results show that better performance can be achieved by using the proposed filter for both texture and depth image denoising, compared with conventional bilateral filter and trilateral filter.