Automatic deblocking method based on sparse representation

Abstract

The invention relates to an automatic deblocking method based on sparse representation, comprising the following steps of: based on the concept of synergetic filtration, repeatedly taking a decoded image block by utilizing a redundancy dictionary obtaining by training; then removing a part of high-frequency information in a sparse solution domain of the image block by utilizing a zero masking technology; newly training the dictionary by utilizing an image obtained by first-time deblocking, and repeating the process until a satisfactory effect is obtained. The method utilizes the concept that multiple-time sampling is carried out in the same position of an air space for acquiring an average and specific information is removed on a dictionary domain through sparse representation. The method comprises the steps of (1) seeking blocking boundaries; (2) processing different boundaries by classification; (3) updating a dictionary D by interaction. The invention can automatically detect the boundaries with blocking effects and adopts different deblocking methods according to different boundary strengths. By utilizing the principle of sparse representation, pixel blocks are recovered by only taking limited coefficients so that false boundary information of the collected pixel block can be removed, and simultaneously the texture information of the original image block can be reserved as much as possible.

Description

technical field [0001] The invention relates to a post-processing after decompressing a grayscale or color image. In particular, it relates to an automatic deblocking method based on sparse representation, which uses sparse representation to remove false boundaries for the blocking effect of decoded images. Background technique [0002] In image compression, the coding method with macroblock as the basic unit is often used, such as JPEG based on discrete cosine transform, image coding based on sparse representation, etc. In order to improve the compression ratio as much as possible, the non-overlapping block taking method is used in this compression method, and the blocks are encoded and decoded independently. Doing so ignores the correlation between blocks, resulting in a decoded image with obvious blockiness. In order to remove the blocking effect, a suitable deblocking filtering strategy can be added at the decoding end to improve the image quality. The existing filter...

AI Technical Summary

Problems solved by technology

The other is to remove the block effect in the DCT domain and use zero mask technology for the DCT domain coefficients of the image block, but this method will cause the image block to lose a lot of high-frequency information and introduce new block effects

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