Compressive Sensed Video Recovery Using Iterative Thresholding with Quality Enhancing Neural Networks
Evgeny Belyaev · 2024
This paper is dedicated to application of video enhancing neural networks for recovery perfomance improvement of traditional (neural networks free) iterative shrink-age/thresholding algorithms (ISTA). First, we show that such a network can be periodically used at some iterations of ISTA. Herewith, the quality imrovement can be achieved especially for video sequences having a high motion activity compressed at low bit rates (or sensing rates). Second, we show how this approach can be embedded into CS-JPEG codec which is based on the compressive sensing framework. Finally, experimental results obtained at low bit rates show that comparing to original CS-JPEG codec the proposed approach can improve Peak Signal-to-Noise Ratio (PSNR) up to 2 dB, and Video Multimethod Assessment Fusion (VMAF) up to 17 scores. Comparison with JPEG and H.264/AVC Intra is also provided.