Multiview Video Plus Depth Format Research Articles

No-reference quality metric for HEVC compression distortion estimation in depth maps

Multiview video plus depth (MVD) is the most popular 3D video format due to its efficient compression and provision for novel view generation enabling the free-viewpoint applications. In addition to color images, MVD format provides depth maps which are exploited to generate intermediate virtual views using the depth image-based rendering (DIBR) techniques. Compression affects the quality of the depth maps which in turn may introduce various structural and textural distortions in the DIBR-synthesized images. Estimation of the compression-related distortion in depth maps is very important for a high-quality 3D experience. The task becomes challenging when the corresponding reference depth maps are unavailable, e.g., when evaluating the quality on the decoder side. In this paper, we present a no-reference quality assessment algorithm to estimate the distortion in the depth maps induced by compression. The proposed algorithm exploits the depth saliency and local statistical characteristics of the depth maps to predict the compression distortion. The proposed ‘depth distortion evaluator’ (DDE) is evaluated on depth videos from standard MVD database compressed with the state-of-the-art high-efficiency video coding at various quality levels. The results demonstrate that DDE can be used to effectively estimate the compression distortion in depth videos.

Depth-Based Texture Coding in AVC-Compatible 3D Video Coding

The target of 3D video coding is to compress Multiview Video plus Depth (MVD) format data, which consist of a texture image and its corresponding depth map. In the MVD format, the depth map plays an important role for successful services in 3D video applications, because it enables the user to experience 3D by generating arbitrary intermediate views. The depth map has a strong correlation with its associated texture data, so it can be utilized to improve texture coding efficiency. This paper introduces a novel and efficient depth-based texture coding scheme. It includes depth-based motion vector prediction, block-based view synthesis prediction, and adaptive luminance compensation, which were adopted in an AVC-compatible 3D video coding standard. Simulation results demonstrate that the proposed scheme reduces the total coding bitrates of texture and depth by 19.06% for the coded PSNR and 17.01% for the synthesized PSNR in a P-I-P view prediction structure, respectively.

Scalable Bit Allocation Between Texture and Depth Views for 3-D Video Streaming Over Heterogeneous Networks

In the multiview video plus depth (MVD) coding format, both texture and depth views are jointly compressed to represent the 3-D video content. The MVD format enables synthesis of virtual views through depth-image-based rendering; hence, distortion in the texture and depth views affects the quality of the synthesized virtual views. Bit allocation between texture and depth views has been studied with some promising results. However, to the best of our knowledge, most of the existing bit-allocation methods attempt to allocate a fixed amount of total bit rate between texture and depth views; that is, to select appropriate pair of quantization parameters for texture and depth views to maximize the synthesized view quality subject to a fixed total bit rate. In this paper we propose a scalable bit-allocation scheme, where a single ordering of texture and depth packets is derived and used to obtain optimal bit allocation between texture and depth views for any total target rates. In the proposed scheme, both texture and depth views are encoded using the quality scalable coding method; that is, medium grain scalable (MGS) coding of the Scalable Video Coding (SVC) extension of the Advanced Video Coding (H.264/AVC) standard. For varying target total bit rates, optimal bit truncation points for both texture and depth views can be obtained using the proposed scheme. Moreover, we propose to order the enhancement layer packets of the H.264/SVC MGS encoded depth view according to their contribution to the reduction of the synthesized view distortion. On one hand, this improves the depth view packet ordering when considered the rate-distortion performance of synthesized views, which is demonstrated by the experimental results. On the other hand, the information obtained in this step is used to facilitate optimal bit allocation between texture and depth views. Experimental results demonstrate the effectiveness of the proposed scalable bit-allocation scheme for texture and depth views.

3D planar representation of stereo depth images for 3DTV applications.

The depth modality of the multiview video plus depth (MVD) format is an active research area, whose main objective is to develop depth image based rendering friendly efficient compression methods. As a part of this research, a novel 3D planar-based depth representation is proposed. The planar approximation of multiple depth images are formulated as an energy-based co-segmentation problem by a Markov random field model. The energy terms of this problem are designed to mimic the rate-distortion tradeoff for a depth compression application. A novel algorithm is developed for practical utilization of the proposed planar approximations in stereo depth compression. The co-segmented regions are also represented as layered planar structures forming a novel single-reference MVD format. The ability of the proposed layered planar MVD representation in decoupling the texture and geometric distortions make it a promising approach. Proposed 3D planar depth compression approaches are compared against the state-of-the-art image/video coding standards by objective and visual evaluation and yielded competitive performance.

Adaptive depth truncation filter for MVC based compressed depth image

In multiview video plus depth (MVD) format, virtual views are generated from decoded texture videos with corresponding decoded depth images through depth image based rendering (DIBR). 3DV-ATM is a reference model for the H.264/AVC based multiview video coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding based nature of H.264/AVC in 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized virtual views. This paper presents a low complexity adaptive depth truncation filter to recover the sharp object boundaries of the depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results demonstrate that the sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views.

Depth Image-Based Rendering With Advanced Texture Synthesis for 3-D Video

A depth image-based rendering (DIBR) approach with advanced inpainting methods is presented. The DIBR algorithm can be used in 3-D video applications to synthesize a number of different perspectives of the same scene, e.g., from a multiview-video-plus-depth (MVD) representation. This MVD format consists of video and depth sequences for a limited number of original camera views of the same natural scene. Here, DIBR methods allow the computation of additional new views. An inherent problem of the view synthesis concept is the fact that image information which is occluded in the original views may become visible, especially in extrapolated views beyond the viewing range of the original cameras. The presented algorithm synthesizes these occluded textures. The synthesizer achieves visually satisfying results by taking spatial and temporal consistency measures into account. Detailed experiments show significant objective and subjective gains of the proposed method in comparison to the state-of-the-art methods.