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Video Coding Based on Compressive Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 3345

Special Issue Editors


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Guest Editor
ITMO University, Kronverksky prospekt 49, 197101 Saint-Petersburg, Russia
Interests: video coding and transmission; arithmetic coding; compressive sensing

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Guest Editor
Tampere University; Korkeakoulunkatu 1, 33720 Tampere, Finland
Interests: computational imaging; compressed sensing; efficient signal processing algorithms; image/video restoration and compression
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Special Issue Information

Dear Colleagues,

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
Guest Editors

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Keywords

  • compressive sensing
  • video coding
  • sparse recovery
  • entropy coding
  • video streaming

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Published Papers (1 paper)

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Research

23 pages, 1364 KiB  
Article
An Efficient Compressive Sensed Video Codec with Inter-Frame Decoding and Low-Complexity Intra-Frame Encoding
by Evgeny Belyaev
Sensors 2023, 23(3), 1368; https://doi.org/10.3390/s23031368 - 26 Jan 2023
Cited by 5 | Viewed by 2522
Abstract
This paper is dedicated to video coding based on a compressive sensing (CS) framework. In CS, it is assumed that if a video sequence is sparse in some transform domain, then it could be reconstructed from a much lower number of samples (called [...] Read more.
This paper is dedicated to video coding based on a compressive sensing (CS) framework. In CS, it is assumed that if a video sequence is sparse in some transform domain, then it could be reconstructed from a much lower number of samples (called measurements) than the Nyquist–Shannon theorem requires. Here, the performance of such a codec depends on how the measurements are acquired (or sensed) and compressed and how the video is reconstructed from the decoded measurements. Here, such a codec potentially could provide significantly faster encoding compared with traditional block-based intra-frame encoding via Motion JPEG (MJPEG), H.264/AVC or H.265/HEVC standards. However, existing video codecs based on CS are inferior to the traditional codecs in rate distortion performance, which makes them useless in practical scenarios. In this paper, we present a video codec based on CS called CS-JPEG. To the author’s knowledge, CS-JPEG is the first codec based on CS, combining fast encoding and high rate distortion results. Our performance evaluation shows that, compared with the optimized software implementations of MJPEG, H.264/AVC, and H.265/HEVC, the proposed CS-JPEG encoding is 2.2, 1.9, and 30.5 times faster, providing 2.33, 0.79, and 1.45 dB improvements in the peak signal-to-noise ratio, respectively. Therefore, it could be more attractive for video applications having critical limitations in computational resources or a battery lifetime of an upstreaming device. Full article
(This article belongs to the Special Issue Video Coding Based on Compressive Sensing)
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