By Ee-Leng Tan, Woon-Seng Gan
This ebook first introduces vintage in addition to contemporary computational types for just-noticeable-difference (JND) purposes. because the discrete cosine remodel (DCT) is utilized in lots of photograph and video criteria (JPEG, MPEG-1/2/4, H.261/3), the ebook additionally encompasses a accomplished survey of computational versions for JND which are in accordance with DCT. The visible components utilized in those computational types are reviewed intimately. extra, an in depth comparative research of those versions utilizing quantitative and qualitative functionality standards is gifted, which compares the noise shaping functionality of those versions with subjective evaluate and the accuracy among the anticipated JND thresholds and subjective evaluation.
There are many surveys on hand on computational types for JND; in spite of the fact that, those surveys seldom examine the functionality of computational versions which are in accordance with DCT. The authors’ survey of the computational types and their in-depth evaluation of the visible components utilized in them can assist readers comprehend perceptual photograph coding in line with DCT. The booklet additionally offers a comparative research of numerous perceptual picture coders which are according to DCT, which fit with the hugely renowned and generally followed JPEG standard.
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Extra info for Perceptual Image Coding with Discrete Cosine Transform
Let tðxÞ denote the spatial JND threshold of the pixel located at x, where x ¼ fx1 ; x2 g; x1 ¼ 0; 1; . ; H À 1, and x2 ¼ 0; 1; . ; W À 1. Based on luminance adaptation and contrast masking, their spatial JND proﬁle of an image is given as tYLL ðxÞ ¼ tla ðxÞ þ tcm ðxÞ À Col Â minftla ðxÞ; tcm ðxÞg; ð3:6Þ where tla ðxÞ and tcm ðxÞ are the visibility thresholds due to luminance adaptation and contrast masking at x, respectively; Col accounts for the reduction of spatial JND due to the overlapping effect in masking and 0\Col 1.
W B À 1: The B subband JND proﬁles are estimated as vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ pﬃﬃﬃ pﬃﬃﬃ u BÀ1 X BÀ1 pﬃﬃﬃ u X pﬃﬃﬃ tB ðbi ; sÞ ¼ twB ðbi Þ t 2 s 1 B þ p1 ; s 2 B þ p2 ; ð3:9Þ p1 ¼0 p2 ¼0 where wB ðbi Þ is the weighting factor derived from a parametric CSF. À1 dBÀ1 ða1 Þ ; ð3:10Þ and dB ðbi Þ is computed as dB ð bi Þ ¼ pﬃﬃﬃ pﬃﬃﬃ W = BÀ1 H = BÀ1 X X a1 ¼0 a2 ¼0 " # W bi W p ﬃﬃ ﬃ CSF pﬃﬃﬃ pﬃﬃﬃ þ a1 ; pﬃﬃﬃ mod B ðbi Þ þ a2 ; B B B ð3:11Þ where modb ðaÞ produces the reminder of a divided by b, and CSF denotes a parametric CSF.
The rest of this chapter is organized as follows. The technique to estimate the spatial JND proﬁle from the JND proﬁle computed in DCT subband domain is discussed in Sect. 1. This is followed by a comparison of spatial JND proﬁles of images estimated from the DCT-II domain and computed directly from the pixel domain. A comparative analysis of noise-shaping performance of Chou’s, Yang’s, Watson’s, Wei’s, and Zhang’s JND models is presented in Sect. 2. This is followed by an analysis of contrast sensitivity estimated by the ﬁve JND models.