View-Specific Based Error Concealment Scheme for Multi-view Plus Depth Video

This paper presents an efficient error concealment method for multiview video plus depth (MVD) delivery over error-prone channels based on the view synthesis information. Specifically, we propose an enhanced temporal error concealment (TEC) method by estimating the probable partition and missing MVs of a corrupted macroblock (MB) using the synthesized information. Furthermore, the view synthesis error concealment (VSEC) approach that directly uses the synthesized pixels to conceal the corrupted MBs is also adaptively utilized together with the TEC approach. Experimental results have shown the effectiveness of the proposed method compared with the existing methods in terms of both objective and subjective measures.

Recent developments of three-dimensional (3D) video coding greatly rely on the use of Multiview Video plus Depth (MVD) data format for representing a 3D scene. This type of data can be coded with conventional video compression schemes and can enable advanced 3D video functionality, such as rendering of virtual views at the decoder side. The MVD represents a 3D scene from different viewing angles as video and pixel-wise associated depth data. In this paper we consider the redundancy of depth data in the MVD representation and propose a novel scheme, called Unpaired MVD (UP-MVD) format to be used in 3D video applications. Being a subset of the MVD this new format assumes that a reduced number of depth views compared to the number of texture views can reduce bitrate as well as the encoding/decoding complexity while still providing the 3DV functionality in many scenarios. The simulation results show that the proposed unpaired MVD format outperforms the MVD format on average from 0.9% to 6.95% of Bjontegaard delta bitrate (dBR) for the baseline disparity adjustments from 50% to 100% of the coded baseline, respectively. Moreover, UP-MVD provides equal or better rate-distortion results for all test sequences for up to 20% view separation adjustment, and in five out of seven sequences a better rate-distortion performance is observed when 50% view separation adjustment is applied.

Multi-view video plus depth (MVD) is gathering huge attention, as witnessed by the recent standardization activity, since its rich information about the geometry of the scene allows high-quality synthesis of virtual viewpoints. Distributed video coding of such kind of content is a challenging problem whose solution could enable new services as interactive multi-view streaming. In this work we propose to exploit the geometrical information of the MVD format in order to estimate inter-view occlusions without communication among cameras. Experimental results show a bit rate reduction up to 77% for low bit rate w.r.t. state-of-the-art architectures.

Multiview video plus depth (MVD), which consists of a texture image and its associated depth map, has been introduced as a 3D video format, and 3D video coding, such as 3D-HEVC, was developed to efficiently compress this MVD data. However, this requires high encoding complexity because of the additional depth coding. In particular, intra coding using various prediction modes is very complicated. To reduce the complexity, we propose a fast depth intra mode decision method based on mode analysis. The proposed method adaptively reduces the number of original candidate modes in a mode decision process. Experimental results show that the proposed method achieves high performance in terms of the complexity reduction.

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.

Multi-view video plus depth (MVD) is a 3D video representation. In MVD, the depth map provides the scene distance information and is used to render the virtual view through Depth Image Based Rendering (DIBR) technique. The depth map coding error will induce distortion in the rendered virtual views. This paper proposes a mathematic model that can estimate the synthesized virtual view distortion induced by depth map compression, and the model is employed to the rate distortion optimization (RDO) in the depth map coding. Based on the rendered virtual view quality, a Lagrangian optimization adjustment scheme at Coding Unit (CU) level is proposed to improve the depth map encoding efficiency. Experimental results demonstrate that the proposed method can improve the BD-PSNR of virtual view for 0.62 dB, and the encoding complexity reduces compared with the view synthesis optimization (VSO) technique in the 3D-HEVC Test Model (HTM).

Multi-view video plus depth (MVD) is the promising and widely adopted data representation for future 3D visual applications and interactive media. However, compression distortions on depth videos impede the development of such applications, and filters are crucially needed for the quality enhancement at the terminal side. Cross-view priors can intuitively be involved in filter design, but these priors are also distorted in compression and thus the contribution of them can hardly be considered in previous research. In this article, we propose a cross-view optimized filter for depth map quality enhancement by making full use of inner- and cross-view priors. We dedicate to evaluate the contributions of distorted cross-view priors in filtering the current view of depth, and then both inner- and cross-view priors can be involved in the filter design. Thus, distortions of cross-view priors are not barriers again as before. For the purpose of that, mutual information guided cross-view consistency is designed to evaluate the contributions of cross-view priors from compression distortions of MVD. After that, under the framework of global optimization, both inner- and cross-view priors are modeled and taken to minimize the designed energy function where both data accuracy and spatial smoothness are modeled. The experimental results show that the proposed model outperforms state-of-the-art methods, where 3.289 dB and 0.0407 average gains on peak signal-to-noise ratio and structural similarity metrics can be obtained, respectively. For the subjective evaluations, object details and structure information are recovered in the compressed depth video. We also verify our method via several practical applications, including virtual view synthesis for smooth interaction and point cloud for 3D modeling for accuracy evaluation. In these verifications, the ringing and malposition artifacts on object contours are properly handled for interactive video, and discontinuous object surfaces are restored for 3D modeling. All of these results suggest that compression distortions in MVD can be properly filtered by the proposed model, which provides a promising solution for future bandwidth constrained 3D and interactive visual applications.

In multi-view video plus depth (MVD) format based 3D video (3DV) systems, the quality of the rendered virtual views depends highly on the quality of the compressed depth maps. Therefore, depth coding technology is vital for 3DV applications. This paper proposed a superpixel based non-regular homogenous block (NRHB) representation for low bit rate depth map coding. At the encoder side, with the aid of the superpixel segmentation of the associated texture images and the synthesis distortion estimation of the virtual views, the original depth maps are partitioned into several NRHBs and represented with only a small amount of pixels. Then, the NRHB represented depth maps are compressed by using the standard video coding approach afterward. At the decoder side, the depth maps are reconstructed through position mapping of the superpixel segmentation of the decoded texture images, and enhanced by a false edge filter. The experimental results show that the proposed method could impressively increase both the coding efficiency of the depth maps and the rendering quality of the virtual views. It also can be seen that the proposed method significantly reduced the encoding computational complexity for depth maps, which makes it particularly useful for the low bandwidth mobile consumer devices with limited computational resources.

Adaptive depth truncation filter for MVC based compressed depth image

In free viewpoint video (FVV) and 3DTV, the depth image-based rendering method has been put forward for rendering virtual view video based on multi-view video plus depth (MVD) format. However, the projection with slightly different perspective turns the covered background regions into hole regions in the rendered video. This paper presents a depth enhanced image summarization generation model for the hole-filling via exploiting the texture fidelity and the geometry consistency between the hole and the remaining nearby regions. The texture fidelity and the geometry consistency are enhanced by drawing texture details and pixel-wise depth information into the energy cost of similarity measure correspondingly. The proposed approach offers significant improvement in terms of 0.2dB PSNR gain, 0.06 SSIM gain and subjective quality enhancement for the hole-filling images in virtual viewpoint video.

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.

Compressed video bit-streams are extremely sensitive to packet loss over error-prone channels. Error concealment (EC) has been considered as one of error control techniques to improve the reconstructed picture quality against transmission errors. However, EC methods show poor image reconstruction performance due to unavailable scene change information of the video sequence, especially when an abrupt scene change occurs. In this paper, we propose an effective EC method based on scene change detection algorithm (SCDA), which provides information to decide whether spatial or temporal EC is better to be used for intra and inter frames respectively. The simulation results show that the proposed method highly improves the subjective quality of incorrectly decoded frames and obtains an average gain of 0.7dB compared with the H.264/AVC Joint Model reference software.

In this paper, a prescription-based error concealment (PEC) method is proposed. PEC relies on pre-analyses of the concealment error image (CEI) for I-frames and the optimal error concealment scheme for P-frames at encoder side. CEI is used to enhance the image quality at decoder side after error concealment (by spatial interpolation or zero motion) of the corrupted intra-coded MBs. A set of pre-selected error concealment methods is evaluated for each corrupted inter-coded MB to determine the optimal one for the decoder. Both the CEI and the scheme indices are considered as the prescriptions for decoder and transmitted along with the video bit stream based on a reversible data embedding technique. Experiments show that the proposed method is capable of achieving PSNR improvement of up to 1.48 dB, at a considerable bit-rate, when the packet loss rate is 20%

Region-based error concealment of right-view frames for stereoscopic video transmission

In this paper, the error concealment techniques as motion vector extrapolation (MVE), boundary matching (BM), side matching (SM), 16/spl times/16-MB-based and 8/spl times/8-block-based concealment are implemented and compared with feedback-based no waiting ARQ (NWARQ) error recovery technique. A new error concealment method using preprocessing (PREP) and search is proposed and implemented. This new method tries to use the information of surrounding MV as much as possible, and find a more precise motion vector using additional search processing in a search window centered by the estimated motion vector. Simulation results, based JM1.4 codec of JVT, show that both NWARQ and PREP can significantly improve the reconstruction video quality. And the combined PREP, SEARCH and NWARQ techniques can greatly achieve improved performance. From the simulation results, the SM and 16/spl times/16-MB-based concealment methods show better performance than BM and 8/spl times/8-block-based concealment.