Spatio-Temporal Just Noticeable Distortion Profile for Grey Scale Image/Video in DCT Domain

Abstract

In image and video processing field, an effective compression algorithm should remove not only the statistical redundancy information but also the perceptually insignificant component from the pictures. Just-noticeable distortion (JND) profile is an efficient model to represent those perceptual redundancies. Human eyes are usually not sensitive to the distortion below the JND threshold. In this paper, a DCT based JND model for monochrome pictures is proposed. This model incorporates the spatial contrast sensitivity function (CSF), the luminance adaptation effect, and the contrast masking effect based on block classification. Gamma correction is also considered to compensate the original luminance adaptation effect which gives more accurate results. In order to extend the proposed JND profile to video images, the temporal modulation factor is included by incorporating the temporal CSF and the eye movement compensation. Moreover, a psychophysical experiment was designed to parameterize the proposed model. Experimental results show that the proposed model is consistent with the human visual system (HVS). Compared with the other JND profiles, the proposed model can tolerate more distortion and has much better perceptual quality. This model can be easily applied in many related areas, such as compression, watermarking, error protection, perceptual distortion metric, and so on.

JPEG-based Variable Block-Size Image Compression using CIE La*b* Color Space

“…where coeff_max is the matrix containing maximum values that DCT coefficients can take for a given range of spatial frequencies. Stimulus required to generate the matrix [36] is; (17) where sgn = +1 or -1 depending on the argument. Above equation can be modified if the range of intensity values is from -X to +X, as in the case of a* and b* planes.…”

“…Quantization value for the DC coefficient is chosen depending upon the maximum value attained by the DC coefficient of the DCT sub-block that is calculated using (17) and (18). As quoted in [45], a human being can distinguish at the most 100 distinct luminance values (L max = 100).…”

Structure similarity image quality assessment based on visual perception

“…Safranek [4] had been widely used in digital watermarking [5,6] and digital image compression [7] and other technical fields [8].…”

“…In-band visual sensitivity Sin band(r, B) expresses visual sensitivity degree of the human eyes to various sub-band image which caused by brightness and texture mask effect [7], [8].…”

Interpreting MOS scores, when can users see a difference? Understanding user experience differences for photo quality

“…These two JND categories are the subband-domain JND models and the pixel-wise JND models. For the subband-domain models, the image needs to be first transformed into a subband domain, such as the DCT-domain [9,21]. On the other hand, pixel-domain methods [13,19,24,26,29] are directly calculated on the spatial domain and thus are more convenient and less computationally complex.…”