Decoding H.264/AVC using prior information and source constraints

The H.264/AVC standard employs a number of errorresilient mechanisms to correct transmission errors. These methods assume a packet-loss scenario, where all the macroblocks (MBs) contained within a corrupted slice are dropped and concealed. However, most of the MBs contained within corrupted slices provide minimal (if any) visual distortions and therefore concealing them causes a superfluous drop in the quality of the recovered video content. This paper presents a novel error control mechanism which employs prior information and residual source-redundancy to recover the most-likelihood feasible H.264/AVC bitstream. Simulation results show that the algorithm recovers a number of corrupted sequences and achieves overall Peak Signal-to-Noise Ratio (PSNR) gains between 1dB and 2dB over the standard. The proposed solution is compatible with the H.264/AVC with no additional bandwidth requirements.peer-reviewe

Distributed coding of endoscopic video

Triggered by the challenging prerequisites of wireless capsule endoscopic video technology, this paper presents a novel distributed video coding (DVC) scheme, which employs an original hash-based side-information creation method at the decoder. In contrast to existing DVC schemes, the proposed codec generates high quality side-information at the decoder, even under the strenuous motion conditions encountered in endoscopic video. Performance evaluation using broad endoscopic video material shows that the proposed approach brings notable and consistent compression gains over various state-of-the-art video codecs at the additional benefit of vastly reduced encoding complexity

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly-reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%-94% saving in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further, to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kbps for the transport of the required compressed video information to the system implementing the CNN models

Robust video transmission using reversible watermarking techniques

This paper presents a novel error-resilient strategy which employs a reversible watermarking technique to protect the H.264/AVC video content. The proposed scheme adopts reversible watermarking to embed an error detection codeword within every Macro block (MB). The watermark is then extracted at the decoder and used to detect the corrupted MBs to be concealed. The proposed scheme further manages to recover the original video content after watermark extraction, thus providing no loss in video quality. The simulation results demonstrate that the proposed approach provides a substantial gain of up to 2.6 dB in Peak Signal-to-Noise Ratio (PSNR) relative to the standard with a minimal increase in complexity.peer-reviewe