Abstract
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.
Highlights
Image processing technology has always been a research hotspot in the field of computer science
Image compression technology can use limited storage space to save a larger proportion of image data; at the same time, it can reduce the data size of images of the same quality, which can effectively improve the efficiency of network data transmission. e traditional image compression technology includes two independent parts, image acquisition and image compression, which limit the fusion improvement method of the two correlated compression technology parts. e emergence of compressed sensing (CS) theory breaks the above frame of image compression, and it completes the image acquisition and compression in the step of sparse observation synchronously; on the one hand, it simplifies the image processing process, and on the other hand, it provides new research areas for image fusion compression
We proposed a JPEG lifting algorithm based on the adaptive block compressed sensing (ABCS), and named it as JPEG-ABCS. is proposed algorithm focuses on the following aspects: (1) guiding best morphological blocking by minimizing mean information entropy (MIE); (2) generating an element vector of subimage pixels using the texture feature and 2dimensional direction DCT; (3) selecting the dimension of the measurement matrix by variance and local significance factors; (4) rate control by matching the overall sampling rate and the quantization matrix; (5) realizing iterative reconstruction of a minimum error under noise condition by using noise influence model analysis
Summary
Image processing technology has always been a research hotspot in the field of computer science. Is paper focuses on the research of image compression algorithms with a JPEG similar structure and improves them with the combination of CS technology. Mathematical Problems in Engineering reasonably use the a priori information between pixels of different subimage blocks to reduce interpixel redundancy In the end, the former JPEG-like algorithms fail to eliminate psychological visual redundancy by considering overall and local saliency. Is proposed algorithm focuses on the following aspects: (1) guiding best morphological blocking by minimizing mean information entropy (MIE); (2) generating an element vector of subimage pixels using the texture feature and 2dimensional direction DCT; (3) selecting the dimension of the measurement matrix by variance and local significance factors; (4) rate control by matching the overall sampling rate and the quantization matrix; (5) realizing iterative reconstruction of a minimum error under noise condition by using noise influence model analysis.
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