Parametric coding of texture in multi-view videos for 3DTV
02 Mar 2015-pp 1-5
TL;DR: The proposed scheme exploits the perceptual redundancy of the textured region in a statistical manner which is ignored in the multi-view video coding scheme of H.264/MVC.
Abstract: In this paper, a parametric texture based video coding scheme is proposed for multi-view videos. The proposed scheme exploits the perceptual redundancy of the textured region in a statistical manner which is ignored in the multi-view video coding scheme of H.264/MVC. This statistical nature of texture is independent of the 3D scene structure. The set of most similar texture macroblocks over different frames is termed as a motion thread. In the proposed scheme, each P slice/frame of multi-view video is categorized into non-texture blocks which are encoded by H.264/MVC and texture blocks which are encoded by our novel approach. These texture based motion threads are encoded with the help of Spatio-Temporal Autoregressive (STAR) model. These motion threads covers the temporal and interview movement of macroblocks maintaining consistency while STAR model exploits the temporal and inter-view perceptual redundancy in neighborhood. The proposed scheme achieves average gain as high as 4.9 dB PSNR over H.264/MVC for given multi-view videos.
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01 Jan 2001
4,379 citations
"Parametric coding of texture in mul..." refers methods in this paper
...The PSNR gain from RD curves is shown in table I & III for the performance comparison [13]....
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TL;DR: An experimental analysis of multiview video coding (MVC) for various temporal and inter-view prediction structures is presented, showing that prediction with temporal reference pictures is highly efficient, but for 20% of a picture's blocks on average prediction with reference pictures from adjacent views is more efficient.
Abstract: An experimental analysis of multiview video coding (MVC) for various temporal and inter-view prediction structures is presented. The compression method is based on the multiple reference picture technique in the H.264/AVC video coding standard. The idea is to exploit the statistical dependencies from both temporal and inter-view reference pictures for motion-compensated prediction. The effectiveness of this approach is demonstrated by an experimental analysis of temporal versus inter-view prediction in terms of the Lagrange cost function. The results show that prediction with temporal reference pictures is highly efficient, but for 20% of a picture's blocks on average prediction with reference pictures from adjacent views is more efficient. Hierarchical B pictures are used as basic structure for temporal prediction. Their advantages are combined with inter-view prediction for different temporal hierarchy levels, starting from simulcast coding with no inter-view prediction up to full level inter-view prediction. When using inter-view prediction at key picture temporal levels, average gains of 1.4-dB peak signal-to-noise ratio (PSNR) are reported, while additionally using inter-view prediction at nonkey picture temporal levels, average gains of 1.6-dB PSNR are reported. For some cases, gains of more than 3 dB, corresponding to bit-rate savings of up to 50%, are obtained.
645 citations
"Parametric coding of texture in mul..." refers methods in this paper
...The existing compression scheme for multi-view video is referred as multi-view video coding (MVC) which exploits temporal and inter-view redundancy [1]....
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16 Sep 1996
TL;DR: This work model image sequences of temporal textures using the spatio-temporal autoregressive model (STAR), which expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time.
Abstract: Temporal textures are textures with motion. Examples include wavy water, rising steam and fire. We model image sequences of temporal textures using the spatio-temporal autoregressive model (STAR). This model expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time. The model provides a base for both recognition and synthesis. We show how the least squares method can accurately estimate model parameters for large, causal neighborhoods with more than 1000 parameters. Synthesis results show that the model can adequately capture the spatial and temporal characteristics of many temporal textures. A 95% recognition rate is achieved for a 135 element database with 15 texture classes.
352 citations
"Parametric coding of texture in mul..." refers methods in this paper
...Based on the correlation of the textured region, a Spatio-Temporal Autoregressive (STAR) model [8] of a particular order is applied to the texture region to represent as a linear combination of spatial, temporal and inter-view neighborhood region....
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TL;DR: Experiments are presented that demonstrate the benefits of FMO as an error resilience tool in case of packet loss over IP networks and a quantitative assessment of this cost is presented for a number of scenarios.
160 citations
"Parametric coding of texture in mul..." refers background or methods in this paper
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01 Jan 2006
TL;DR: Experimental coding results show that view synthesis prediction has the potential to perform significantly better than both disparity compensated view prediction and independent coding of all views using H.264/AVC.
Abstract: We consider multiview video compression: the problem of jointly compressing multiple views of a scene recorded by different cameras. To take advantage of the correlation between views, we compare the performance of disparity compensated view prediction and view synthesis prediction to independent coding of all views using H.264/AVC. The proposed view synthesis prediction technique works by first synthesizing a virtual version of each view using previously encoded views and using the virtual view as a reference for predictive coding. We present experimental coding results showing that view synthesis prediction has the potential to perform significantly better than both disparity compensated view prediction and independent coding of all views. Accepted for publication in Picture Coding Symposium 2006 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2006 201 Broadway, Cambridge, Massachusetts 02139 Publication History:– 1. First printing, TR2006-035, April 2006 View Synthesis for Multiview Video Compression Emin Martinian, Alexander Behrens, Jun Xin, and Anthony Vetro email:{martinian,jxin,avetro}@merl.com, behrens@tnt.uni-hannover.de Mitsubishi Electric Research Labs 201 Broadway Cambridge, MA 02139, USA Abstract. We consider multiview video compression: the problem of jointly compressing multiple views of a scene recorded by different cameras. To take advantage We consider multiview video compression: the problem of jointly compressing multiple views of a scene recorded by different cameras. To take advantage of the correlation between views, we compare the performance of disparity compensated view prediction and view synthesis prediction to independent coding of all views using H.264/AVC. The proposed view synthesis prediction technique works by first synthesizing a virtual version of each view using previously encoded views and using the virtual view as a reference for predictive coding. We present experimental coding results showing that view synthesis prediction has the potential to perform significantly better than both disparity compensated view prediction and independent coding of all views. Index Terms view synthesis, view interpolation, multiview video compression, H.264/AVC
133 citations
"Parametric coding of texture in mul..." refers background in this paper
...In multi-view videos, the motion threads are spanned from a particular view to the next view in a zigzagged manner [11] as shown in figure 2....
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