Adaptive block compressing sensing image coding method based on visual perception

A technology of visual perception and coding method, which is applied in the field of adaptive block compression sensing image coding based on visual perception, and can solve problems such as application, large observation matrix, and ignoring local sparsity of images

Inactive Publication Date: 2013-04-10
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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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 an

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  • Adaptive block compressing sensing image coding method based on visual perception
  • Adaptive block compressing sensing image coding method based on visual perception
  • Adaptive block compressing sensing image coding method based on visual perception

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Embodiment Construction

[0087] The visual perception-based adaptive block compression sensing image coding system proposed by the invention is realized by software simulation.

[0088] exist figure 1 In we give an implementation block diagram of the proposed system of the present invention. After the image is input, the image is divided into blocks, and the divided image block x i First, the traditional block compressed sensing (BCS) method is used for observation (all blocks use the same observation rate, that is, Φ Wni = Φ W ), observed value y i It is transmitted to the decoding end for overall reconstruction and block reconstruction, where the coefficients after overall reconstruction The restored image of the initial stage is obtained through post-processing, and the block of DCT coefficients obtained after block reconstruction Carry out the visual analysis and classification of the first stage, and at the same time, transmit the classification result to the encoding end through the feedb...

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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 (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 generally implemented throug...

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Application Information

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IPC IPC(8): H04N7/26H04N19/176
Inventor 李志宏王安红张雪刘磊
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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