A Measure Method for Adaptive Distortion in Space-Time Domain

Abstract

The invention discloses a measurement method for a space-time domain self-adaptive just noticeable distortion. The method is characterized by comprising the following steps: step one, integrating pattern masking effect PM and luminance adaptation LA to obtain a space-domain JND (Just Noticeable Distortion) threshold JNDS; step two, integrating four time domain sensing parameters including relativemovement I (vr), background movement U(vg), time domain duration I (Tau) and residual fluctuation intensity U (Delta) to obtain a time-domain JND adjusting weight coefficient z; and step three, performing adjusting by using the time-domain JND adjusting weight coefficient z on the basis of the space-domain JND threshold JNDS, thereby obtaining a space-time domain JND threshold JNDST.

Description

technical field [0001] The invention belongs to the field of image and video, and in particular relates to a space-time domain self-adaptive just perceptible distortion measurement method. Background technique [0002] The Human Visual System (HVS, Human Visual System) has perceptual characteristics such as temporal masking, spatial masking, contrast sensitivity, brightness adaptability, visual attention, central foveal characteristics, and recency effect. These HVS perceptual characteristics affect the observer's perception of target images or the subjective perception of the video, thereby affecting the subjective perceived quality of the image or video. [0003] Due to the various masking effects of the human eye, the human eye can only detect noise / distortion exceeding a certain threshold, that is, just noticeable distortion (JND: Just Noticeable Distortion), which is the minimum perceivable distortion threshold. The current mainstream JND models are mainly divided into...

AI Technical Summary

Problems solved by technology

[0006] This type of method directly applies the contrast sensitivity function to the model, which can better describe the sensitivity of the human eye to different frequency components, but the disadvantage of this type of method is that the transformation block divides the correlation between image blocks and blocks. can only use the correlation between pixels in the block

[0010] (1) The current pixel domain estimation method does not consider comprehensively, especially the relationship between temporal domain pixels and the influence of inter-frame motion factors on the JND model;

[0011] (2) There are many factors affecting human visual perception, and the influence of human visual interest, space-time masking, foveal characteristics and recency effect on JND modeling has not been fully considered;

[0012] In the bottom-to-top analysis method, by analyzing various features in video images, the impact of these features on JND perception is quantitatively evaluated based on human visual characteristics; however, it is difficult to accurately integrate image features of different dimensions

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