Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery
This paper presents a compressive-sensing- (CS-) based video codec which is suitable for wireless video system requiring simple encoders but tolerant, more complex decoders. At the encoder side, each video frame is independently measured by block-based random matrix, and the resulting measurements a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/562840 |
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| _version_ | 1850213885289693184 |
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| author | Ran Li Hongbing Liu Rui Xue Yanling Li |
| author_facet | Ran Li Hongbing Liu Rui Xue Yanling Li |
| author_sort | Ran Li |
| collection | DOAJ |
| description | This paper presents a compressive-sensing- (CS-) based video codec which is suitable for wireless video system requiring simple encoders but tolerant, more complex decoders. At the encoder side, each video frame is independently measured by block-based random matrix, and the resulting measurements are encoded into compressed bitstream by entropy coding. Specifically, to reduce the quantization errors of measurements, a nonuniform quantization is integrated into the DPCM-based quantizer. At the decoder side, a novel joint reconstruction algorithm is proposed to improve the quality of reconstructed video frames. Firstly, the proposed algorithm uses the temporal autoregressive (AR) model to generate the Side Information (SI) of video frame, and next it recovers the residual between the original frame and the corresponding SI. To exploit the sparse property of residual with locally varying statistics, the Principle Component Analysis (PCA) is used to learn online the transform matrix adapting to residual structures. Extensive experiments validate that the joint reconstruction algorithm in the proposed codec achieves much better results than many existing methods with consideration of the reconstructed quality and the computational complexity. The rate-distortion performance of the proposed codec is superior to the state-of-the-art CS-based video codec, although there is still a considerable gap between it and traditional video codec. |
| format | Article |
| id | doaj-art-879e9f2a6beb4abcb67f52e39dab7cf6 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-879e9f2a6beb4abcb67f52e39dab7cf62025-08-20T02:09:02ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/562840562840Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual RecoveryRan LiHongbing LiuRui XueYanling LiThis paper presents a compressive-sensing- (CS-) based video codec which is suitable for wireless video system requiring simple encoders but tolerant, more complex decoders. At the encoder side, each video frame is independently measured by block-based random matrix, and the resulting measurements are encoded into compressed bitstream by entropy coding. Specifically, to reduce the quantization errors of measurements, a nonuniform quantization is integrated into the DPCM-based quantizer. At the decoder side, a novel joint reconstruction algorithm is proposed to improve the quality of reconstructed video frames. Firstly, the proposed algorithm uses the temporal autoregressive (AR) model to generate the Side Information (SI) of video frame, and next it recovers the residual between the original frame and the corresponding SI. To exploit the sparse property of residual with locally varying statistics, the Principle Component Analysis (PCA) is used to learn online the transform matrix adapting to residual structures. Extensive experiments validate that the joint reconstruction algorithm in the proposed codec achieves much better results than many existing methods with consideration of the reconstructed quality and the computational complexity. The rate-distortion performance of the proposed codec is superior to the state-of-the-art CS-based video codec, although there is still a considerable gap between it and traditional video codec.https://doi.org/10.1155/2015/562840 |
| spellingShingle | Ran Li Hongbing Liu Rui Xue Yanling Li Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery International Journal of Distributed Sensor Networks |
| title | Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery |
| title_full | Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery |
| title_fullStr | Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery |
| title_full_unstemmed | Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery |
| title_short | Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery |
| title_sort | compressive sensing based video codec by autoregressive prediction and adaptive residual recovery |
| url | https://doi.org/10.1155/2015/562840 |
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