Adaptive Block Compression Sensing Image Coding Method Based on Visual Perception

Abstract

Disclosed is a low-complex adaptive block compressing sensing image coding method based on visual perception. The low-complex adaptive block compressing sensing image coding method based on visual perception is characterized in that I. Block compressing sensing: 1. Coding: reading images and separating images; block observing; sending observed values and observed rates to decoding end; 2. Decoding: measurement matrix is obtained from the observed rates; finding an initial solution; wiener filtering; updated by procedural language (PL); discrete cosine transform (DCT) switching; bivariate threshold denoising disposing; inverse discrete cosine transform (IDCT); updated by the PL; repeated till finish of initial phrase; II. Visual analysis: analyzing each refactoring DCT coefficient block; classifying blocks; entering feedback channel; III. The adaptive block compressing sensing instructed by visual perception: 1. Coding: adaptive observe according to feedback results;sending observed values and observed rates to decoding end; 2. Decoding: combining the observed values; last phrase recover image as a first value; wiener filtering; updated by PL; DCT switching; bivariate threshold denoising disposing; IDCT; updated by the PL; till end of the decoding; next phrase analysis, adaptive observe and refactoring, till the recover image meets the need.

Description

technical field [0001] The invention belongs to the technical field of image coding methods, and in particular relates to a low-complexity visual perception-based adaptive block compression sensing image coding method. Background technique [0002] Compressive sensing (Compressive Sensing, abbreviated as CS) theory is a new signal processing method that has just emerged in recent years. Its advantage is that the amount of data sampled by CS is much smaller than that obtained by traditional sampling methods, which breaks through the bottleneck of Shannon's sampling theorem and makes it possible to collect high-resolution signals. The CS theory requires that the signal itself is compressible or can be sparsely represented in a certain transform domain, using non-adaptive linear projection to maintain the original structure of the signal, and then accurately reconstructing the original signal through numerical optimization problems. [0003] Since the sampling in CS is general...

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Problems solved by technology

[0003] Since the sampling in CS is generally implemented through a random matrix, once it is applied to a two-dimensional image, it will face the following problems: First, the observation matrix is ​​relatively large, which will require high memory resources and increase the amount of calculation; The transformation of the image ignores the local sparsity of the image in the spatial position and time domain, resulting in system performance degradation

However, it has not yet been seen how to apply visual features in CS

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