Top-down natural image just noticeable distortion threshold estimation method

Abstract

The invention discloses a top-down just-noticeable distortion threshold estimation method for a natural image. The method comprises the following steps of: partitioning and vectorizing a grayscale image of a source image to obtain a vectorized matrix; obtaining a covariance matrix of the vectorization matrix and eigenvalues and eigenvectors of the covariance matrix, and arranging the eigenvectors from large to small according to the eigenvalues to obtain a KLT kernel; calculating a KLT coefficient matrix, KLT coefficient energy, normalized KLT coefficient energy and accumulated normalized KLT coefficient energy, and calculating a perceptual undistorted critical point according to a derived perceptual undistorted critical point calculation equation; constructing a perception undistorted coefficient reconstruction matrix, and reconstructing to obtain a perception undistorted coefficient matrix; converting vectors of each dimension in the perceptual distortion-free coefficient matrix into image blocks and splicing the image blocks again to obtain a perceptual distortion-free critical image, and further obtaining a just perceptible distortion threshold graph; the method has the advantages that the visual masking characteristic of a human visual system can be well reflected, and the visual perception redundancy of a natural image can be well described.

Description

technical field [0001] The present invention relates to a natural image just noticeable distortion (Just Noticeable Distortion, JND) threshold estimation technology, in particular to a top-down natural image just noticeable distortion threshold estimation method, which is based on a top-down design Idea, and use KLT (Karhunen-Loéve Transform) transformation technology to realize the threshold estimation of just perceivable distortion of natural images. Background technique [0002] Just Noticeable Distortion (JND) refers to the maximum change amplitude of the visual signal that the Human Visual System (HVS) cannot perceive. It reflects the sensitivity of the human visual system (HVS) to changes in visual information and the potential perceptual redundancy in visual signals. This makes it widely used in many image / video perceptual processing tasks, including image / video compression, image / video enhancement, information hiding, and image / video evaluation, etc. Just because o...

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Problems solved by technology

However, there are some inherent limitations in such a design idea: first, due to the lack of a deep and comprehensive understanding of the characteristics of the human visual system (HVS), it is difficult to take all potentially relevant influencing factors into account; second, the considered The incoming influencing factors are often difficult to accurately describe through simple mathematical models; third, the interrelationships between different influencing factors are also difficult to model

Therefore, the existing just noticeable distortion (JND) threshold estimation model is often difficult to achieve satisfactory results, although people can explore more influencing factors and corresponding visual masking effects through experiments, and at the same time use more accurate mathematical models to model them, but such work is endless

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