Region-of-interest-based image dark field brightness JND value determination method and prediction method

Abstract

The invention discloses a region-of-interest-based image dark field brightness JND (Just Noticeable Difference) value determination method and prediction method. The determination method includes the steps that firstly, an original image is converted into an xyY space, linear compression is conducted on the component Y according to different coefficients of compressibility, the dark field brightness of the original image is increased, and a set of test images is obtained; the visual perception experiment is conducted, and at least one test image is found out to serve as a JND critical image; next, the image dark field brightness JND value based on the region-of-interest is worked out according to the region-of-interest weight map of the original image. The invention further discloses a region-of-interest-based image dark field brightness JND value prediction method, wherein 0.20cd / m<2> serves as the image dark field brightness JND prediction value based on the region-of-interest of the image. By means of the region-of-interest-based image dark field brightness JND value determination method and the prediction method, quantitative evaluation and improvement can be conducted on display parameters, especially the influence, which human eyes can perceive, on the image dark field brightness when the parameters of the dark field brightness and contrast ratio are designed, and thus a basis is provided for display technology design and research.

Description

technical field [0001] The present invention relates to a method for measuring a JND (Just Noticeable Difference) value of dark field brightness of an image, in particular to a method for measuring a JND value of dark field brightness of an image based on an area of ​​interest. Background technique [0002] Display technology can not only realize accurate, intuitive, clear and fast information output, but also provide a friendly interface for man and machine to exchange information for the information age. Market research shows that display image quality is one of the most important determinants for end users to purchase electronic display devices. Therefore, the evaluation of information display quality is an important content in the field of display technology. Since all displayed information is observed by the observer through human eyes, people's judgments on image quality are different for different environments and uses. Therefore, the human factor must be considered...

AI Technical Summary

Problems solved by technology

Improving the contrast can be solved by increasing the brightness in the bright field and reducing the brightness in the dark field. However, it is very difficult to increase the brightness and increase the power consumption. The easiest way to improve the contrast is to reduce the brightness of the dark field.

However, the existing dark-field luminance JND measurement technology is difficult to apply to any natural image, for example, DICOM [Digital Imaging and Communications in Medicine (DICOM), Standard-PS 3.14-2003 [S], Part 14: Grayscale Standard Display Function, ( 2003), p21-27] standards released the human eye's perception threshold of brightness changes under different brightness conditions, but it was carried out under strict observation conditions, and the test chart of uniform color blocks was used instead of natural images

Xia Jun et al [Just-noticeable-difference of influential factors of subjective images quality, Xia Jun, Qin Shaoling, Liu Lu, Yin Hanchun, Journal of Southeast University (Natural Science Edition) , 36(5), 2006, p695-699] also did a preliminary study on the JND of dark field brightness. Only two natural images were selected, and the experimental method used was the paired comparison method, which can only give the dark field brightness Rough Interval Values ​​for JND

Images