Special Issue "Video Coding Based on Compressive Sensing"
Deadline for manuscript submissions: 15 June 2022.
Interests: video coding and transmission; arithmetic coding; compressive sensing
Interests: computational imaging; compressed sensing; efficient signal processing algorithms; image/video restoration and compression
Special Issues and Collections in MDPI journals
According to compressive sensing (CS) frameworks, if a signal is sparse in some transform domain, then it can be recovered from a much smaller number of samples than the Nyquist–Shannon theorem requires. This enables potentially wide opportunities in the development of new cheap sensors, including tiny video encoding devices. Potentially, CS-based video coding methods have the following advantages. First, at the encoder side, it could be enough to perform a linear transformation, select a few coefficients located at pseudo-random positions (called measurements), and send them to the decoder. A computational complexity of such operation could be comparable to JPEG encoding complexity. Second, the measurements could be coded and transmitted independently from each other. As a result, a large number of bit stream scalability layers can be supported, and loss of some measurements (due to packet losses in a communication channel) does not affect other delivered measurements which can be used for decoding without an error propagation effect. However, existing video codecs based on CS are signiﬁcantly inferior in terms of rate-distortion performance to conventional codecs, such as H.264/AVC or H.265/HEVC. Moreover, CS recovery algorithms require relatively high computational complexity, which makes it difficult to perform them in real-time. This Special Issue is addressed at the new approaches which help to overcome the above- listed limitations of the existing CS video codecs.
Dr. Evgeny Belyaev
Prof. Dr. Karen Egiazarian
Manuscript Submission Information
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- compressive sensing
- video coding
- sparse recovery
- entropy coding
- video streaming