Just noticeable distortion model based on entropy masking in DCT domain

Abstract

In order to improve the threshold accuracy of JND (just noticeable distortion) model in DCT (discrete cosine transform) domain and avoid cross-domain operation, entropy masking effect was introduced into DCT-based JND model.Firstly, starting from the free-energy theory and the Bayesian inference, an autoregressive model based on texture-energy similarity in DCT domain was designed to simulate the spontaneous prediction behavior of visual perception.Secondly, the mapping relationship between visual perception and prediction residuals were explored to obtain the disorder intensity in block level.Thirdly, the entropy masking effect was modeled as a JND threshold modulation factor of disorder intensity.Finally, the JND model in DCT domain for the entropy masking was proposed by fusing the contrast sensitivity function, the luminance adaptive masking, the contrast masking.Compared with the existing JND model in DCT domain, the proposed model performed all operations in DCT domain, which was more efficient and concise.The subjective and objective experimental results indicate that the proposed JND model shows greater tolerance to distortion with better perceptual quality.

Keywords

• JND;human visual system;entropy masking effect;free-energy theory;Bayesian inference