The Experts below are selected from a list of 34650 Experts worldwide ranked by ideXlab platform
B. Bai - One of the best experts on this subject based on the ideXlab platform.
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Multiview Video Compression
2009Co-Authors: B. BaiAbstract:With the progress of computer graphics and computer vision technologies, 3D/multiview Video applications such as 3D-TV and tele-immersive conference become more and more popular and are very likely to emerge as a prime application in the near future. A successful 3D/multiview Video system needs synergistic integration of various technologies such as 3D/multiview Video acquisition, Compression, transmission and rendering. In this thesis, we focus on addressing the challenges for multiview Video Compression. In particular, we have made 5 major contributions: (1) We propose a novel neighbor-based multiview Video Compression system which helps remove the inter-view redundancies among multiple Video streams and improve the performance. An optimal stream encoding order algorithm is designed to enable the encoder to automatically decide the stream encoding order and find the best reference streams. (2) A novel multiview Video transcoder is designed and implemented. The proposed multiview Video transcoder can be used to encode multiple compressed Video streams and reduce the cost of multiview Video acquisition system. (3) A learning-based multiview Video Compression scheme is invented. The novel multiview Video Compression algorithms are built on the recent advances on semi-supervised learning algorithms and achieve Compression by finding a sparse representation of images. (4) Two novel distributed source coding algorithms, EETG and SNS-SWC, are put forward. Both EETG and SNS-SWC are capable to achieve the whole Slepian-Wolf rate region and are syndrome-based schemes. EETG simplifies the code construction algorithm for distributed source coding schemes using extended Tanner graph and is able to handle mismatched bits at the encoder. SNS-SWC has two independent decoders and thus can simplify the decoding process. (5) We propose a novel distributed multiview Video coding scheme which allows flelxible rate allocation between two distributed multiview Video encoders. SNS-SWC is used as the underlying Slepian-Wolf coding scheme. It is the first work to realize simultaneous Slepian-Wolf coding of stereo Videos with the help of a distributed source code that achieves the whole Slepian-Wolf rate region. The proposed scheme has a better rate-distortion performance than the separate H.264 coding scheme in the high-rate case.
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ICME - An Efficient Multiview Video Compression Scheme
2005 IEEE International Conference on Multimedia and Expo, 1Co-Authors: B. Bai, Pierre Boulanger, Janelle HarmsAbstract:Multiview Video Compression is important to the image-based 3D Video applications. In this paper, we proposes a novel neighbor-based multiview Video Compression scheme. It is essentially a MPEG2-like block-based scheme. In particular, a method to decide the stream encoding order is presented. The resulting stream encoding order can better decorrelate spatial redundancies among multiple Video streams than the center approach. Experimental results confirm the superiority of the proposed neighbor approach over the center approach and MPEG2 for multiview Video Compression
Janelle Harms - One of the best experts on this subject based on the ideXlab platform.
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ICME - An Efficient Multiview Video Compression Scheme
2005 IEEE International Conference on Multimedia and Expo, 1Co-Authors: B. Bai, Pierre Boulanger, Janelle HarmsAbstract:Multiview Video Compression is important to the image-based 3D Video applications. In this paper, we proposes a novel neighbor-based multiview Video Compression scheme. It is essentially a MPEG2-like block-based scheme. In particular, a method to decide the stream encoding order is presented. The resulting stream encoding order can better decorrelate spatial redundancies among multiple Video streams than the center approach. Experimental results confirm the superiority of the proposed neighbor approach over the center approach and MPEG2 for multiview Video Compression
Lubomir Bourdev - One of the best experts on this subject based on the ideXlab platform.
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Learned Video Compression
2019 IEEE CVF International Conference on Computer Vision (ICCV), 2019Co-Authors: Oren Rippel, Sanjay Nair, Steve Branson, Alexander Anderson, Lubomir BourdevAbstract:We present a new algorithm for Video coding, learned end-to-end for the low-latency mode. In this setting, our approach outperforms all existing Video codecs across nearly the entire bitrate range. To our knowledge, this is the first ML-based method to do so. We evaluate our approach on standard Video Compression test sets of varying resolutions, and benchmark against all mainstream commercial codecs in the low-latency mode. On standard-definition Videos, HEVC/H.265, AVC/H.264 and VP9 typically produce codes up to 60% larger than our algorithm. On high-definition 1080p Videos, H.265 and VP9 typically produce codes up to 20% larger, and H.264 up to 35% larger. Furthermore, our approach does not suffer from blocking artifacts and pixelation, and thus produces Videos that are more visually pleasing. We propose two main contributions. The first is a novel architecture for Video Compression, which (1) generalizes motion estimation to perform any learned compensation beyond simple translations, (2) rather than strictly relying on previously transmitted reference frames, maintains a state of arbitrary information learned by the model, and (3) enables jointly compressing all transmitted signals (such as optical flow and residual). Secondly, we present a framework for ML-based spatial rate control - a mechanism for assigning variable bitrates across space for each frame. This is a critical component for Video coding, which to our knowledge had not been developed within a machine learning setting.
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learned Video Compression
arXiv: Image and Video Processing, 2018Co-Authors: Oren Rippel, Sanjay Nair, Steve Branson, Carissa Lew, Alexander G Anderson, Lubomir BourdevAbstract:We present a new algorithm for Video coding, learned end-to-end for the low-latency mode. In this setting, our approach outperforms all existing Video codecs across nearly the entire bitrate range. To our knowledge, this is the first ML-based method to do so. We evaluate our approach on standard Video Compression test sets of varying resolutions, and benchmark against all mainstream commercial codecs, in the low-latency mode. On standard-definition Videos, relative to our algorithm, HEVC/H.265, AVC/H.264 and VP9 typically produce codes up to 60% larger. On high-definition 1080p Videos, H.265 and VP9 typically produce codes up to 20% larger, and H.264 up to 35% larger. Furthermore, our approach does not suffer from blocking artifacts and pixelation, and thus produces Videos that are more visually pleasing. We propose two main contributions. The first is a novel architecture for Video Compression, which (1) generalizes motion estimation to perform any learned compensation beyond simple translations, (2) rather than strictly relying on previously transmitted reference frames, maintains a state of arbitrary information learned by the model, and (3) enables jointly compressing all transmitted signals (such as optical flow and residual). Secondly, we present a framework for ML-based spatial rate control: namely, a mechanism for assigning variable bitrates across space for each frame. This is a critical component for Video coding, which to our knowledge had not been developed within a machine learning setting.
Xin Jin - One of the best experts on this subject based on the ideXlab platform.
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learning for Video Compression
IEEE Transactions on Circuits and Systems for Video Technology, 2020Co-Authors: Zhibo Chen, Xin JinAbstract:One key challenge to learning-based Video Compression is that motion predictive coding, a very effective tool for Video Compression, can hardly be trained into a neural network. In this paper, we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network. On the basis of PMCNN, we further explore a learning-based framework for Video Compression with additional components of iterative analysis/synthesis and binarization. The experimental results demonstrate the effectiveness of the proposed scheme. Although entropy coding and complex configurations are not employed in this paper, we still demonstrate superior performance compared with MPEG-2 and achieve comparable results with H.264 codec. The proposed learning-based scheme provides a possible new direction to further improve Compression efficiency and functionalities of future Video coding.
Didier Le J Gall - One of the best experts on this subject based on the ideXlab platform.
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the mpeg Video Compression algorithm
Signal Processing-image Communication, 1992Co-Authors: Didier Le J GallAbstract:Abstract The Video Compression technique developed by MPEG covers many applications from interactive systems on CD-ROM to delivery of Video information over telecommunications networks. The MPEG Video Compression algorithm relies on two basic techniques: block based motion compensation for the reduction of the temporal redundancy and transform domain based Compression for the reduction of spatial redundancy. Motion compensation techniques are applied with both predictive and interpolative techniques. The prediction error signal is further compressed with spatial redundancy reduction (DCT). The quality of the compressed Video with the MPEG algorithm at about 1.5 Mbit/s has been compared to that of consumer grade VCR's.
