Method for encoding progressive image based on adaptive block compressed sensing

A technology of image coding and sensor coding, applied in the field of image coding, which can solve the problems of not considering the change of channel bandwidth, not considering the influence of characteristic recovery quality, and the application limitation of CS theory and technology.

Inactive Publication Date: 2011-08-10
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

However, in the block compression sensing algorithm, the characteristics of different blocks in the image and their influence on the restoration quality are not considered. At

Method used

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  • Method for encoding progressive image based on adaptive block compressed sensing
  • Method for encoding progressive image based on adaptive block compressed sensing
  • Method for encoding progressive image based on adaptive block compressed sensing

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Embodiment Construction

[0083] Ⅰ. Basic layer

[0084] The basic layer of the present invention mainly divides the read-in image into blocks, observes the image block with a small fixed observation rate, and decodes at the decoding end to obtain a basically clear restored image. The specific operation steps are:

[0085] 1. Basic layer coding:

[0086] The first step: read in an image, and divide the image into multiple non-overlapping image blocks of B×B size;

[0087] Step 2: Generate a B 2 ×B 2 Orthogonal Gaussian random matrix of size as the seed observation matrix Φ;

[0088] The third step: use formula (1) to perform CS observation for each image block:

[0089] the y i = Φ B x i , (1)

[0090] Among them, Φ B is the i-th image block x i The observation matrix of , by extracting the former M of the seed observation matrix Φ B what you get, MR is the set observation rate; y i is x i observations of all y i Form the set of observations y;

[0091] Step 4: ...

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Abstract

The invention provides a method for encoding a progressive image based on adaptive block compressed sensing, belonging to the technical field of image ending. The method is characterized by comprising two encoding layers, namely a basic layer and a reinforced layer, wherein the basic layer is used for reading an image and dividing the image into blocks, observing the image blocks, sending observation values and observation rates to a decoding terminal so as to obtain an observation matrix according to the observation rates and obtain an initial solution according to the observation values and the observation matrix, carrying out Wiener filtering, updating with a picewise linear (PL) algorithm, carrying out contourlet wavelet transformation, carrying out bivariate shrinkage threshold processing on a wavelet coefficient, carrying out inverse wavelet transformation, updating with the PL algorithm, and repeatedly iterating until decoding is finished; and the reinforced layer is used for classifying the image blocks, observing the different classes of blocks with different observation rates, sending observation values and observation rates to the decoding terminal, using the restoration image of the basic layer as an initial value of the current decoding, carrying out the Wiener filtering, updating with the PL algorithm, carrying out contourlet wavelet transformation, carrying out bivariate shrinkage threshold processing, carrying out inverse wavelet transformation, updating with the PL algorithm, repeatedly iterating until the decoding is finished, and then carrying out observation and decoding of the next stage until the restoration image meets the requirements.

Description

technical field [0001] The invention belongs to the technical field of image coding methods, in particular to a progressive image coding method based on adaptive block compression sensing. Background technique [0002] Compressed Sensing (Compressed Sensing, CS) is a novel signal processing method that has just been developed in recent years. The CS theory points out that as long as the signal is compressible or sparse in a certain transform domain, the transformed high-dimensional signal can be projected onto a low-dimensional space by using an observation matrix unrelated to the transform base, and then through Solving an optimization problem allows the original signal to be reconstructed with high probability from these few projections. Its core idea is to combine compression and sampling. First, the non-adaptive linear projection (measurement value) of the signal is collected, and then the original signal is reconstructed from the measurement value according to the corr...

Claims

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Application Information

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IPC IPC(8): H04N7/26H04N19/30
Inventor 王安红刘磊刘丽赵美朱孔粉
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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