Y data compression processing method, device, equipment and WebP compression system

Abstract

The invention discloses a YUV image Y data compression processing method, and relates to the field of image processing. The YUV image Y data compression processing method comprises the following steps: acquiring Y macro block residual data of an input image; performing DCT transformation on the Y macro block residual data to obtain an AC coefficient; adding all subblock data matrixes in the macroblock residual data to obtain a DC component matrix of the macro block residual data; performing WHT transformation on the DC component matrix to obtain a DC coefficient; and quantifying the AC coefficient and the DC coefficient to obtain Y data compression data. Through the adoption of the method, the parallel execution of the DCT transformation and the WHT transformation can be realized, the pipeline parallel characteristics of modules are improved, and the overall throughput capacity of a WebP image lossy compression algorithm is improved. The invention further discloses a YUV image Y datacompression processing device and equipment and a WebP lossy compression system based on a YUV image. The YUV image Y data compression processing device and equipment and the WebP lossy compression system have the above-mentioned beneficial effects.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a YUV image Y data compression processing method, device, equipment and a YUV image-based WebP lossy compression system. Background technique [0002] With the development of image acquisition equipment such as mobile phones, tablets, and digital cameras, and the increase in image pixel scale, the scale of Internet image data has grown exponentially. The latest research shows that from 2016 to 2021, the scale of data storage on data center servers will increase fourfold, from 663EB to 2.6ZB. Most of the data storage comes from images and videos, and image processing is used for data storage on data center servers. and network bandwidth will pose serious challenges. [0003] At present, in order to reduce the size of image storage, the image file format WebP with a higher compression rate is often used for storage. Compared with the JPEG lossy compression algorithm, the W...

AI Technical Summary

Problems solved by technology

[0006] From the Y macroblock residual data compression process, there is a data dependency between the sub-block DCT transformation and WHT transformation. After the DCT transformation of all 4x4 sub-blocks in the Y 16x16 macroblock is completed, the DC components of all sub-blocks can be generated. , and then the WHT transformation process of the DC component matrix can be started, resulting in that the DCT transformation and the WHT transformation can only be executed serially, and the deeply pipelined parallel WebP image lossy compression algorithm FPGA heterogeneous acceleration cannot be realized

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