A Block Adaptive Carton Image Compression Method Based on Dictionary Learning

Abstract

The invention relates to the technical field of image processing, and discloses a block-based adaptive carton image compression method based on dictionary learning. The method uses learning dictionary, image segmentation, edge detection, adaptive setting error, and secondary optimization of sparse coefficient matrix Carry out encoding and compression; use the K-SVD algorithm to train an offline dictionary on the carton image sample set, divide the image into blocks and use the improved Canny edge detection to obtain the contour area, determine the structural complexity of the image block, and set the block adaptively to set sparseness Indicates the error of the model, and at the same time uses the OMP algorithm to calculate the initial sparse coefficient matrix of the image block under the original error, then optimizes the sparse coefficient matrix twice according to the adaptive error, and finally extracts the non-zero value of the sparse coefficient matrix and its index for encoding and compression , this design can complete image data sparseness and reconstruction, reduce computer storage resources occupied by image data, and reduce storage costs.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a block-adaptive carton image compression method based on dictionary learning. Background technique [0002] With the rapid development of image acquisition technology in the fields of monitoring, medicine, remote sensing, etc., the collected image data has increased significantly, so it is of great significance to study image compression to facilitate image transmission and storage. Since the JPEG image compression standard was proposed It has been widely used in just a few years. At present, most of the image compression methods built into electronic devices used in our daily life are JPEG; [0003] In recent years, the use of sparse coefficient matrices and overcomplete dictionaries to compress images on the basis of compressed sensing has received considerable attention. The existing methods are all image compression based on dictionary learning. By improving the ma...

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Problems solved by technology

[0003] In recent years, compressing images using sparse coefficient matrices and over-complete dictionaries on the basis of compressed sensing has received considerable attention. The existing methods are image compression based on dictionary learning, through improved matching pursuit algorithms, optimized learning dictionaries and other methods Improve the sparsity of the sparse matrix to achieve high compression of the image, but the structural characteristics of the image are not fully utilized when solving the sparse coefficient matrix, which is prone to the following deficiencies:

[0004] 1. Taking the carton picture as an example, when reconstructing the carton image, more consideration is given to the text information such as the brand of the goods on the picture. Therefore, using the same compression ratio to compare areas with few features, compressed storage is a waste of physical resources;

[0005] 2. Areas with rich image features need to retain more detailed information. As early as 2012, it was proposed to use a targeted dictionary for a specific type of image to adaptively compress the image. The input image is encoded. This scheme is based on a sparse dictionary structure. The compact representation of the structure makes the transmission cost of the dictionary and compressed data relatively low, but this method transmits the dictionary and compressed data together, which increases the image compression rate;

[0006] 3. Subsequently, on the previous basis, someone proposed a multi-view image compression method based on the local patch dictionary, which divides the image into small blocks, applies sparse coding to each small block, and uses dictionary atoms to construct the selected The vector of the region represents overlapping small blocks. This technique does not need to store or transmit dictionary atoms. This method has better compression performance than JPEG2000, but it is only for video images with continuous frames, and cannot adaptively compress images.

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