Distributed Video Coding using Compressive Sampling

Prades-Nebot, J.; Ma, Yi; Huang, Thomas S.

doi:10.1109/pcs.2009.5167431

Cited by 106 publications

(112 citation statements)

References 7 publications

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“…• Applying conventional coding schemes (MPEG/H.264) to key frames and acquire local block-based and global frame-based CS measurements for non-key frames as suggested in [23,32].…”

Section: Discussionmentioning

confidence: 99%

“…• Using a dictionary for decoding [32] where a dictionary is used for comparison and prediction of non-key frames. Similarly, a dictionary can be learned from neighboring frames for reconstruction of non-key frames [33].…”

Section: Discussionmentioning

confidence: 99%

“…A dictionary based distributed approach to CVS is reported in [32]. Video frames are divided into key frames and non-key frames.…”

Section: Dictionary Based Compressed Video Sensingmentioning

confidence: 99%

See 2 more Smart Citations

Compressive Video Coding: A Review of the State-Of-The-Art

Baig¹,

Lai²,

Punchihewa³

2012

Video Compression

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“…• Applying conventional coding schemes (MPEG/H.264) to key frames and acquire local block-based and global frame-based CS measurements for non-key frames as suggested in [23,32].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Compressive Video Coding: A Review of the State-Of-The-Art

Baig¹,

Lai²,

Punchihewa³

2012

Video Compression

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“…Here, three coding modes of the block are designed, that is, SKIP mode, SINGLE mode and L1 mode, which have been improved compared with [9]. For the odd frames, the standard encoder can be used without any modifications.…”

Section: Overviewmentioning

confidence: 99%

“…In [8], a novel framework called distributed compressed video sensing (DISCOS) is proposed as a solution for distributed video coding based on the recently emerging compressed sensing theory. In [9], a new DVC based on CS principles is proposed. At the encoder, each CS frame (non-key frame) is divided into nonoverlapping blocks and then sampled, quantized and transmitted.…”

Section: Introductionmentioning

confidence: 99%

Region Adaptive Measurements for Distributed Compressive Video Sensing

Hong-yan¹

2016

Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications

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Abstract. Due to limited resources, wireless video sensor networks (WVSN) needs low complexity methods to realize video capture and compression. Recently, distributed video coding (DVC) has emerged to reduce the video encoding complexity with a certain coding efficiency. Furthermore, compressive sensing (CS) has been proposed to directly capture the compressed data efficiently. In this paper, combining DVC and CS, a novel video coding framework has been designed for WVSN. In order to preserve low complexity at the encoder, odd frames of the original video can be compressed as key frames by standard intra-coding while even frames can be processed as CS frames by CS encoder. Here, flexible mode selection of the encoder is applied to improve coding efficiency. Furthermore, adaptive measurements are allocated for different blocks in view of the object and background regions in the video frames. At the decoder, the bi-directional dictionary is proposed as the sparse basis to improve the recovery quality of CS. The experimental results validate the effectiveness of the proposed scheme with better performance than other compared schemes.

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Scalable Video Coding Using Compressive Sensing

Jiang¹,

Haimi-Cohen³

et al. 2012

Bell Labs Tech. J.

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the client has ample computational resources and a high resolution display, there is no way to get a better viewing experience which is commensurate with the available resources. This phenomenon is known as the "cliff effect"-no video is available unless some threshold constraints are met and no improvement is achieved when the constraints are exceeded. Scalable video coding (SVC) [18] encodes video into ordered layers, where each higher layer provides a refinement to the encoding of the lower layers. Since decoding of lower layers is a prerequisite for the decoding of higher ones, the lower layers need a higher level of protection in transmission. In broadcast applications, each layer may be transmitted as a separate stream, with different protection levels, e.g., by using hierarchical modulation [9]. In point-to-point applications, feedback from the client usually indicates how many layers are to be transmitted. In these situations, providing different protection to each layer is usually difficult-all layers receive the same protection, which severely limits the scalability. The practical limitation of SVC is its relatively small number of layers (due to

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Distributed Video Coding using Compressive Sampling

Cited by 106 publications

References 7 publications

Compressive Video Coding: A Review of the State-Of-The-Art

Compressive Video Coding: A Review of the State-Of-The-Art

Region Adaptive Measurements for Distributed Compressive Video Sensing

Scalable Video Coding Using Compressive Sensing

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