Method for extracting area-of-interest of video based on HVS

Abstract

The invention discloses a method for extracting an area-of-interest of a video based on an HVS. The method comprises the steps that firstly, the time domain information amount and the space domain information amount of the video are measured according to the inter-frame correlation and the intra-frame correlation of the video; secondly, the time domain information amount representing the time domain feature of the video and the space domain information amount representing the space domain feature of the video are fused according to the feature fusion strategy so that video information amount distribution can be obtained; thirdly, the video information amount is mapped into the vision salience degree through a vision hysterisis mapping model based on the HVS; finally, the area-of-interest of the video is obtained through the vision salience degree of the video by means of self-adaption threshold judgment. By the adoption of the feature mapping model based on the HVS, video features can be converted into the video salience degree fitting in with the feature of human vision, the time domain feature and the space domain feature of the video can be measured through the information amount, the algorithm complexity is low, and the algorithm precision is high.

Description

technical field [0001] The invention relates to a video signal processing method, in particular to an HVS-based video interest region extraction method. Background technique [0002] The evaluation of video quality by end users of video services is the most reliable result. However, this subjective evaluation method is not real-time, time-consuming and cumbersome. In actual research and engineering, a variety of objective evaluation methods combined with the visual characteristics of the human eye are used to simulate the subjective evaluation effect. In this way, the results matching the subjective evaluation are obtained. [0003] Human visual characteristics include visual spatial characteristics and temporal characteristics, which are determined by the structural characteristics of the human eye. From the perspective of neurobiology, the visual sensitivity of the human eye determines the sensory quality changes of images and videos in the human eye. In still images, ...

AI Technical Summary

Problems solved by technology

However, the mapping between visual saliency and interest is not linear. For example, two important human visual characteristics: motion sensitivity and visual hysteresis, these two characteristics will affect video saliency

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