Underwater image compression using energy based adaptive block compressive sensing for IoUT applications

Monika, R.; Dhanalakshmi, Samiappan; Kumar, R.

doi:10.1007/s00371-020-01884-8

Cited by 17 publications

(1 citation statement)

References 33 publications

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“…In recent years, more and more applications where a video upstreaming device is limited by its resources, such as the battery lifetime and the platform performance, have been developed. Examples include deep space missions [ 1 ], wireless endoscopy [ 2 ], wireless multimedia sensors [ 3 , 4 ], the Internet of Underwater Things [ 5 ], and high-speed video capturing [ 6 ]. The basic solution for these applications is traditional block-based intra-frame video coding, such as x264 [ 7 ], which is an open-source implementation of video encoding according to the H.264/AVC [ 8 ] standard.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Compressive Sensed Video Codec with Inter-Frame Decoding and Low-Complexity Intra-Frame Encoding

Belyaev

2023

Sensors

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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.

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