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Overview of Inter-Component Coding in 3D-HEVC
Overview of Inter-Component Coding in 3D-HEVC
Journal of Broadcast Engineering. 2015. Jul, 20(4): 545-556
Copyright © 2015, The Korean Society of Broadcast Engineers
  • Received : May 19, 2015
  • Accepted : June 24, 2015
  • Published : July 30, 2015
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About the Authors
민우 박
진영 이
찬열 김
dionism@gmail.com

Abstract
A HEVC-compatible 3D video coding method (3D-HEVC) has been recently developed as an extension of the high efficiency video coding (HEVC) standard. In order to efficiently deal with the multi-view video plus depth (MVD) format, 3D-HEVC exploits an inter-component prediction which allows the prediction between texture and depth map images in addition to a temporal prediction used in the conventional single layer video coding such as H.264/AVC and HEVC. The performance of the inter-component prediction is normally affected by the accuracy of the disparity vector, and thus it is important to have an accurate disparity vector used for the inter-component prediction. This paper, therefore, introduces a disparity derivation method and inter-component algorithms using the disparity vector for the efficient 3D video coding. Simulation results show that the 3D-HEVC provides higher coding performance compared with the simulcast approach using HEVC and the simple multi-view extension (MH-HEVC).
Keywords
Ⅰ. Introduction
This paper introduces a 3D video coding method (3D-HEVC) as an extension of the high efficiency video coding (HEVC) standard, which has been recently developed in the ITU-T/ISO/IEC Joint Collaborative Team on 3D Video Coding Extension Development (JCT-3V) standardization group [1] . Along with the advances in 3D video displays, such as a glasses-free 3D television, a free-view point television (FTV) [2] , and so on, these displays need to have multiple views (more than two views) to provide more realistic 3D experience to users. In order to represent a glasses-free 3D video content on these kinds of displays, a relatively large number of views need to be sent and displayed, because it needs to display a stereo view pair in all the positions within the possible viewing angles. However, in the real 3D application such as a 3D video broadcasting and a 3D video player it is far more difficult to handle a large number of views because of the limitation of bandwidth or capacity of the digital media.
In order to deal with the multiple views efficiently, a multi-view video plus depth (MVD) format as a new 3D video representation format was introduced. MVD consists of only a small number (two or three) of texture images and their associated depth map images, and corresponding camera parameters [3] . The MVD format allows a client to generate intermediate texture views between received and reconstructed texture images by using their associated depth map images based on the view synthesis algorithm such as the depth-image-based rendering (DIBR) technique [4] . As the new 3D representation format was introduced and the HEVC standard, which has double the compression ratio compared to the Advanced Video Coding (H.264/ AVC) standard [5] at the same visual quality level, was finalized in January 2013 [6] , the JCT-3V standardization group started to develop the HEVC-compatible 3D video coding standard as the 3D extension of HEVC for efficient compression of the MVD format and finalized it in February 2015.
The multi-view video coding standard such as the Multi-view Video Coding (MVC) standard [5] uses an inter- view prediction, which only allows the prediction across multiple texture view images in the same time instance, without block-level changes. However, 3D-HEVC exploits an inter-component prediction which additionally allows the prediction across texture and depth map images in addition to the inter-view prediction. This paper introduces a disparity derivation method and inter-component coding tools using the disparity in 3D-HEVC. The inter-component algorithms consist of depth-based disparity vector derivation, inter- view motion information prediction, view synthesis prediction, illumination compensation, residual prediction, and depth-based block partitioning. The results demonstrate that 3D-HEVC improves the coding performance by exploiting the inter-component coding tools.
The remaining paper is organized as follows. Section Ⅱ presents the general coding structure of 3D-HEVC. Section Ⅲ explains the depth-based disparity vector derivation method. In section Ⅳ, the inter-component coding tools are explained in detail. Finally, section Ⅴ shows a simulation results and section Ⅵ concludes the paper, respectively.
Ⅱ. Coding Structure and Algorithms
Figure 1 illustrates an example of a general coding structure of 3D-HEVC, which normally has three texture images with their associated depth map images as shown in Fig. 1 . The texture image of view 0 in Fig. 1 , which is a base view, is always encoded with the HEVC encoder to have a backward compatibility with HEVC. The dependent view (view 1 and view 2 in Fig. 1 ) can use the inter-component predictions between texture and depth map images in the same time instance, that is, the same access unit (AU), as shown in Fig. 1 .
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3D-HEVC의 일반적인 부호화 구조 Fig. 1. General coding structure of 3D-HEVC
After encoding of all texture and depth map images, the encoded data is integrated by a multiplexer to a 3D-HEVC bitstream [7] . The 3D-HEVC bitstream is designed to simultaneously support various view-points such as a 2D video display, a stereoscopic video display and a glassesfree 3D video display by the sub-stream extraction process [6] .
For instance, for displaying a 3D video content on a conventional 2D video display, only the texture image of view 0 can be extracted from the 3D-HEVC bitstream and then the extracted bitstream can be decoded using the HEVC decoder. For a stereoscopic video display, two texture views of either view 0 and view 1, or view 0 and view 2 can be extracted from the 3D-HEVC bitstream and then the extracted bitstream can be decoded using the 3D-HEVC decoder. For a glasses-free 3D display, the 3D-HEVC bitstream can be decoded all the texture and depth map images using the 3D-HEVC decoder and then N intermediate views can be additionally generated by using reconstructed texture and depth map images.
Ⅲ. Disparity Vector Derivation
In order to access the coded information such as motion parameters and reconstructed samples of the texture and the depth map images in the reference views, the disparity vector needs to be derived for both the texture and the depth map coding. Moreover, the performance of the inter-component prediction is normally affected by the accuracy of the disparity vector, and thus it is important to derive the accurate disparity vector.
The disparity vector derivation for the texture coding consists of two steps; the first step is the derivation from neighboring blocks and the second step is the refinement of the derived disparity vector [8] - [10] .
The disparity vector is firstly derived from the temporal and spatial neighboring blocks as shown in Fig. 2 . The temporal neighboring blocks are the co-located blocks in the co-located picture defined in the slice header and the random access point (RAP) picture which only has the blocks which performed the inter-view prediction and the spatial neighboring blocks are the left and the above blocks of the current block [11] - [12] . The detailed operation of the first step is illustrated as follows.
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시차 벡터 유도를 위한 주변 블록들 Fig. 2. Neighboring blocks for disparity vector derivation
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After obtaining the disparity vector from neighboring blocks, the derived disparity vector is refined by the corresponding depth block. Since the depth map image of the reference view is already available, more accurate disparity vector can be derived from the corresponding depth block in the reference view. Fig. 3 shows how to derive the corresponding depth block. The corresponding depth block is detected by the disparity vector derived from neighboring blocks (DV in Fig. 3 ). The representative depth value among the four corner depth samples in the corresponding depth block is chosen and then is converted into the disparity vector by using the camera parameters of the current view. And the vertical component of the refined disparity vector is set to zero because the depth value only considers a horizontal shifting [14] . If the corresponding depth block is located outside or on the boundary of the depth map picture, the sample position of the corresponding block located outside of the picture is clipped to the boundary position of the picture [15] .
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현재 블록에 대응하는 깊이 블록 유도 방법 Fig. 3. Derivation of corresponding depth block
The disparity vector is also needed for the depth map coding. However, the accuracy of the disparity vector for the depth map block coding is less important than the texture block coding. Therefore, the disparity vector is derived in slice level. The constant depth value 128, which means the middle value of the depth value range in 8 bit-depth image, is converted into the disparity vector by using corresponding camera parameters. The derived disparity vector is used for the whole blocks in a slice [16] .
Ⅳ. Algorithms Using Inter-Component Prediction
The disparity vector described in the previous section is used for the coding algorithms which exploit the inter- component prediction. In this section, several inter- component coding tools using the disparity vector are discussed.
- 1. Inter-view motion information prediction
The motion information of the corresponding block in the reference view pointed by the disparity vector can be used as the motion information of the current block, because the motion information between the current block and the corresponding block is likely to be very similar if they have the same object. The inter-view motion information prediction (IVMP) mode is added as an additional merge candidate into the merge candidate list.
For the texture block coding, the motion information derivation can be conducted in sub-block level from the corresponding block as shown in Fig.4 . The current block is divided into 8x8 sub-blocks and, the motion information is derived for each sub-block and then the motion compensation is performed for each sub-block using the derived motion information [17] . The motion information of the bottom-right block of the corresponding block can be additionally used as a merge candidate.
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서브블록 단위 시점간 움직 정보 예측 방법의 예 Fig. 4. Example of IVMP in sub-block level
Since the IVMC modes always performs the temporal prediction, when the tool which only performs the inter- view prediction such as the illumination compensation is used, then the IVMC mode is not inserted into merge candidate list [18] .
- 2. View synthesis prediction
The view synthesis prediction (VSP) is the sub-block level inter-view prediction tool using the depth information. Fig. 5 shows an example of the VSP process. The current block is firstly divided into the sub-blocks. The sub-block size is decided to either 8x4 or 4x8 block by comparing the gradient of four corner depth samples in the corresponding depth block of the reference view pointed by the disparity vector from neighboring blocks. And then the disparity vector for each sub-block is derived through the corresponding depth sub-block in the same manner with the second step of the disparity derivation process. After obtaining the disparity vector for each sub-block, the disparity compensation prediction for each sub-block is conducted by using the disparity vector [19] - [20] .
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시점 합성 예측 방법의 예 Fig. 5. Example of VSP
VSP is used as an additional merge candidate in the merge candidate list. There are two types of VSP candidate; one is the default VSP candidate and the other is the derived VSP candidate from spatial neighboring blocks. If a spatial neighboring merge candidate is coded with VSP, then the corresponding candidate is set to the VSP candidate as the derived VSP candidate. Otherwise, the default VSP candidate is set to the VSP candidate.
- 3. Illumination compensation
The illumination compensation (IC) is used to compensate for the illumination mismatch between the current tex ture block and the reference texture block in the reference view picture [21] - [22] . IC is performed by the linear regression model using the neighboring samples ( NBcurr and NBref ) of the current and reference blocks as shown in Fig. 6 expressed as following equation.
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현재 블록의 주변 샘플(NBcurr)과 참조 블록의 주변 샘플(NBref) Fig. 6. Neighboring samples of current block and reference block (NBcurr and NBref)
where (x, y) represents a sample position in the reference block, Pred(x, y) and Pred'(x, y) represent a sample value of the reference block in the reference view and an illumination compensated sample value of the reference block, respectively, a indicates a scale factor and b indicates an offset of the linear regression model.
The scaling factor ( a ) and the offset ( b ) of the linear regression model can be calculated as follows:
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where N represents the number of available neighboring samples, NBcurr(i) and NBref(i) mean a neighboring sample of the current block and the reference block, respectively. λ means the error term, which considers outliers in neighboring samples.
IC is applied only when the partition mode of the current block is PART_2Nx2N mode. In addition, IC does not perform together with the residual prediction to reduce the complexity in terms of the number of operations and the required external memory bandwidth. In order to further optimize IC and the residual prediction, when the residual prediction is enabled, IC is disabled [23] .
- 4. Residual prediction
The residual prediction (RP) predicts the residual data of the current block from the residual data of the corresponding block in the reference view pointed by the disparity vector from the neighboring blocks.
The residual data of the corresponding block is not stored in the memory, but is derived at the time of decoding of the current block in an on-the-fly manner. The corresponding block is identified by the disparity vector for the current block and the residual data of the corresponding block is calculated by performing the motion compensation using the same motion information of the current block, and then the derived residual data is added to the reference block data. RP for the temporal prediction is illustrated in Fig. 7 [24] .
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시간 방향 예측 시 잔차 예측 방법 Fig. 7. RP for temporal prediction
Additionally, RP is extended to the inter-view prediction cases. When the current block performs the inter-view prediction, the residual data of the corresponding block pointed by the motion vector which is derived the similar method to the disparity vector derivation can be generated by conducting the disparity compensation using the disparity vector for the current block, and then the derived residual data is also added to the reference block data. RP for the inter-view prediction is illustrated in Fig. 8 .
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시점 방향 예측 시 잔차 예측 방법 Fig. 8. RP for inter-view prediction
In order to perform RP, total three blocks are needed to fetch from the external memory; the reference block, the corresponding block and the reference block of corresponding block for each reference list (list 0 and list 1). Therefore, the bi-linear interpolation filter is used when obtaining the reference block, the corresponding block and the reference block of the corresponding block to reduce the memory bandwidth requirement in the worst case. In order to further reduce the memory bandwidth requirement, when the current block is 8x8 block size, RP is disabled for chroma component such that the worst case memory bandwidth of 3D-HEVC is less than that of HEVC.
- 5. Depth-based block partitioning
The depth-based block partitioning (DBBP) mode enables an arbitrary block partitioning prediction using the corresponding depth block pointed by the disparity vector [25] . In order to obtain the arbitrary block partition, a binary segmentation mask is generated based on the corresponding depth block in the reference view. A threshold is firstly derived by averaging four corner samples in the corresponding depth block [26] , and then the binary segmentation mask can be generated by using the derived threshold. The depth samples that are greater than the threshold are set to one, which means the foreground region. The other samples are set to zero, which means the background region. Fig. 9 shows an example of generating the binary segmentation mask.
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이진 세그멘테이션 마스크 생성 방법 Fig. 9. Generation of binary segmentation mask [25]
For signaling the motion information of two segments; the foreground and the background regions, the depth-based block partition mode only allows when the partition mode of the current block is PART_2NxN or PART_Nx2N modes [27] .
After the motion or disparity compensation prediction for the foreground and the background regions, the boundaries between two segments are filtered since the region near the boundaries might have the distortion [28] .
The DBBP mode needs to fetch two depth blocks for identifying the corresponding depth block used to generate the segmentation mask in addition to the motion or disparity compensation, and thus the DBBP mode is only allowed in which block size is greater than 8x8. As a result, the worst case memory bandwidth is much less than the worst case of HEVC [29] .
Ⅴ. Experimental Results
In order to examine the coding performance of 3D-HEVC, coding efficiency comparisons with the simulcast approach using HEVC and the multi-view extension of HEVC (MV-HEVC) only allowing the inter-view prediction are carried out based on the common test conditions defined in JCT-3V [30] and the 3D-HEVC reference software 3D-HTM 14.0 [31] . The Bjøntegaard Delta bit-rate (BD-BR), which means an amount of the average bit-rate changes, [32] was used to evaluate the coding performance.
Table 1 shows the coding performance comparison between 3D-HEVC and the HEVC simulcast configuration. “Coded views” indicates the performance of the texture images, and “Synthesized views” indicates the performance of the synthesized images generated by the view synthesis algorithm. As can be seen in Table 1 , 3D-HEVC provides the BD-BR performance of 47.2% and 55.1% for the coded and the synthesized views, respectively. Table 2 shows the coding performance between 3D-HEVC and MV-HEVC. As can be seen in Table 2 , 3D-HEVC provides the BD-BR performance of 13.4% and 17.6% for the coded and the synthesized views, respectively.
3D-HEVC와 HEVC simulcast의 BD-BR 성능 비교Table 1. Comparison of BD-BR results between 3D-HEVC and HEVC simulcast
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3D-HEVC와 HEVC simulcast의 BD-BR 성능 비교 Table 1. Comparison of BD-BR results between 3D-HEVC and HEVC simulcast
3D-HEVC와 MV-HEVC의 BD-BR 성능 비교Table 2. Comparison of BD-BR results between 3D-HEVC and MV-HEVC
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3D-HEVC와 MV-HEVC의 BD-BR 성능 비교 Table 2. Comparison of BD-BR results between 3D-HEVC and MV-HEVC
Table 3 shows the coding performance of each inter- component algorithm described in Section Ⅳ. The inter-component algorithms provide the BD-BR performance of 9.5% and 7.7% for the coded and the synthesized views, respectively. Compared to the comparison results between 3D-HEVC and MV-HEVC, it can be seen that most of the coding gain comes from inter-component coding tools especially for the coded views.
3D-HEVC 인터-컴포넌트 알고리즘들의 BD-BR 성능Table 3. BD-BR performance of inter-component algorithms in 3D-HEVC
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3D-HEVC 인터-컴포넌트 알고리즘들의 BD-BR 성능 Table 3. BD-BR performance of inter-component algorithms in 3D-HEVC
A memory complexity analysis for inter-component algorithms is also conducted based on the method used in [33] . The worst case of HEVC occurs when the bi-predictive 8x8 block is applied. Table 4 shows the worst case of each coding tools in terms of external memory bandwidth requirement. The worst case memory bandwidth of IVMP and IC is 110% to 114% depending on the used memory pattern compared to HEVC, because a depth block fetch is needed in addition to the motion compensation prediction. For VSP, the worst case memory bandwidth is 50% to 81%, since VSP is disallowed in the bi-predictive block. For RP, the worst case memory bandwidth is 87% to 94% since RP uses bi-linear interpolation filter in the motion compensation and the RP for chroma is disabled when the current block is 8x8 block size. For DBBP, the worst case memory bandwidth is 79% to 69% since DBBP is allowed when the block size is greater than 8x8.
메모리 패턴에 따른 외부 메모리 대역 요구사항 분석Table 4. Analysis of external memory bandwidth requirement in the worst case depending on memory patterns
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메모리 패턴에 따른 외부 메모리 대역 요구사항 분석 Table 4. Analysis of external memory bandwidth requirement in the worst case depending on memory patterns
Ⅵ. Conclusion
This paper introduced the disparity derivation method and the inter-component coding tools in 3D-HEVC. 3D-HEVC will be able to efficiently deal with the MVD format to support the next generation 3D video display such as a glasses free 3D television and FTV. The experimental results demonstrated that the 3D-HEVC is more efficient than the HEVC simulcast approach and MV-HEVC.
BIO
박 민 우
- 2003년 02월 : 경희대학교 컴퓨터공학 학사
- 2005년 02월 : 경희대학교 컴퓨터공학 석사
- 2009년 02월 : 경희대학교 컴퓨터공학 박사
- 2009년 03월 ~ 현재 : 삼성전자 DMC연구소 책임연구원
- ORCID : http://orcid.org/0000-0001-8489-8863
- 주관심분야 : 비디오 압축 기술 및 표준화, 영상처리, 멀티미디어 통신
이 진 영
- 2006년 02월 : 성균관대학교 정보통신공학 학사
- 2008년 08월 : 한국과학기술원 전기전자공학 석사
- 2008년 08월 ~ 현재 : 삼성전자 DMC연구소 책임연구원
- ORCID : http://orcid.org/0000-0002-4384-2633
- 주관심분야 : 영상처리, 비디오 압축 기술 및 표준화, 3D 비디오 시스템
김 찬 열
- 1998년 02월 : 연세대학교 전파공학 학사
- 2003년 02월 : 연세대학교 통신공학 석사
- 2009년 09월 : 더블린대학교 전기전자 박사
- 1998년 02월 ~ 현재 : 삼성전자 DMC연구소 수석연구원
- ORCID : http://orcid.org/0000-0002-2337-5177
- 주관심분야 : 비디오 압축 기술 및 표준화, 영상처리
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