Image Compression Method with Variable Quantization Parameters

Abstract

The invention discloses an image compression method having variable quantization parameters, which is used for treating a plurality of pixels of an image. The image compression method having the variable quantization parameters comprises the following steps of: receiving N continuous pixels; obtaining N pixel difference values and N mapping difference values, which correspond to the N pixels, according to values of the N pixels by means of difference pulse-code modulation; obtaining quantization reference values corresponding to the N pixels through calculation according to the N pixel difference values; obtaining the quantization parameters corresponding to the N pixels according to the quantization reference values; and coding the N mapping difference values according to the quantization parameters.

Description

technical field [0001] The invention relates to an image compression method with variable quantization parameters, in particular to an image compression method with variable quantization parameters requiring less memory space. Background technique [0002] With the advancement of electronics and information technology, the development of technologies for processing and displaying images on computers or various electronic devices is becoming more and more common. Early electronic information technology could only store or process low-pixel digital images, but nowadays people have more and more demands for high-quality images. In order to obtain high-quality images, a lossless compression method is often used in image compression. However, high-quality images generally have a large file size, which makes it inconvenient to save and spread. Experts in image processing have developed lossy compression (also known as destructive compression) technology, which discards part of t...

AI Technical Summary

Problems solved by technology

If the existing compression method is used, once the resolution of the image increases, the encoder will need to consume a larger amount of storage space to store a whole row of pixels in the image to compress the image

In other words, the existing image compression methods have problems such as a single quantization parameter is not suitable for the entire image, uneven quantization parameters excessively discard the mid-frequency and high-frequency information of the image, and the required temporary storage space is huge.

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