Abstract:
In this paper we continue to study the problem of efficient video coding based on compressive sensing. In our previous work we proposed CS-JPEG video codec, where dragon ...Show MoreMetadata
Abstract:
In this paper we continue to study the problem of efficient video coding based on compressive sensing. In our previous work we proposed CS-JPEG video codec, where dragon noiselet transform is applied to a differential frame computed as a residual between an input frame and its downsampled and upsampled version. The downsampled version of the frame is compressed via JPEG baseline, while the noiselet coefficients are subsampled, quantized, entropy encoded and embedded into the application header of the JPEG file. At the decoder side, an iterative soft thresholding algorithm (ISTA) with video block-matching and 3D filtering (VBM3D) used as the thresholding operator is applied. In this paper, we first introduce a fast ISTA based on simple temporal and spatial transforms with parameters which are randomly selected at each iteration, and show that the proposed approach reduces the recovery time by around 300 times. Second, we consider the codec parameters selection providing the best reconstruction quality for a given bit rate. Finally, we evaluate the codec performance on HD video sequences recorded by real-life street surveillance systems without camera motion and show that CS-JPEG with the fast decoding and the selected parameters provides 54.3% and 22.4% bit rate savings, in average, comparing with the fast implementations of H.264/AVC Intra or H.265/HEVC Intra, respectively.
Date of Conference: 06-08 October 2021
Date Added to IEEE Xplore: 16 March 2022
ISBN Information: