Screen content image quality evaluation method based on edge and structure characteristics

Abstract

The invention relates to a screen content image quality evaluation method based on edge and structure characteristics, which comprises the following steps of S1, converting an input image from an RGBcolor space to an LMN color space, then acting an imaginary part of a Gabor filter on an L component, and extracting the edge characteristics of the image; S2, converting the input image into a grayscale image, calculating a gradient image on the grayscale image by using a Scharr filter, calculating an LBP image on the gradient image by using a local binary pattern (LBP) operator, and calculatingto obtain structural characteristics of the image on the gradient image according to the LBP image; and S3, according to the two features in the step S1 and the step S2, training an image evaluation model by using a random forest, and predicting the quality scores of all the test images by using the trained model. According to the method, the quality of the image is evaluated by considering the characteristics of a human vision system and combining the edge and structural characteristics of the image, so that the quality evaluation performance of the non-reference image can be remarkably improved.

Description

technical field [0001] The invention relates to the fields of image and video processing and computer vision, in particular to a method for evaluating the image quality of screen content based on edge and structural features. Background technique [0002] With the rapid development of the Internet, Screen Content Image (SCI) is widely used in modern multimedia, such as wireless display, distance education, screen sharing, real-time communication, etc. However, due to technical and equipment defects, distortion often occurs in the process of image acquisition, compression and transmission. In order to better apply screen content images, image quality evaluation becomes particularly important. Many quality assessment methods for this type of image have been proposed so far. For example, Yang proposed a quality assessment method based on uncertainty weighting by considering the different effects of text regions and image regions on the visual quality of SCI. Later, Yang et a...

AI Technical Summary

Problems solved by technology

Ni et al. proposed a quality assessment method for SCI based on the gradient direction. The experimental results show that the method is simple and effective, but it ignores the effects of certain types of distortion, such as brightness changes and contrast changes, and the gradient direction is not sensitive to these

Images