JPEG Lifting Algorithm Based on Adaptive Block Compressed Sensing

Abstract

<jats:p>This paper proposes a JPEG lifting algorithm based on adaptive block compressed sensing (ABCS), which solves the fusion between the ABCS algorithm for 1-dimension vector data processing and the JPEG compression algorithm for 2-dimension image data processing and improves the compression rate of the same quality image in comparison with the existing JPEG-like image compression algorithms. Specifically, mean information entropy and multifeature saliency indexes are used to provide a basis for adaptive blocking and observing, respectively, joint model and curve fitting are adopted for bit rate control, and a noise analysis model is introduced to improve the antinoise capability of the current JPEG decoding algorithm. Experimental results show that the proposed method has good performance of fidelity and antinoise, especially at a medium compression ratio.</jats:p>

A reformative algorithm for compressed sensing sparse signal recovery based on smoothed L0 norm

“…Compressed sensing [1][2][3][4] is a new signal sampling theory that takes full advantage of signal sparsity or compressibility.…”

Research on image analysis and processing method based on compressed perception technology

“…In the literature [14], a controlled arbitrary sampling network is proposed for solving the compressive sensing problem with a single model of arbitrary sampling matrix, and a random projection enhancement strategy is used to improve the training diversity of the sampling space, thus achieving arbitrary sampling and improving computational efficiency as well as generalization capability. In the literature [15], a JPEG enhancement algorithm based on adaptive block compression perception was constructed using mean information entropy and multi-feature saliency index, using the joint model and curve fitting for code rate control, and introducing noise analysis model to solve the problem of JPEG fusion for processing image data.…”