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Evaluation of Region-of-Interest coders using perceptual image quality assessments
Authors:JC Garcia-Alvarez  H Führ  G Castellanos-Dominguez
Affiliation:1. Universidad Nacional de Colombia, Signal Processing and Recognition Group, Manizales, Colombia;2. RWTH Aachen, Lehrstuhl A für Mathematik, Germany
Abstract:A perceptual measure emulates the human vision for image quality assessment. This paper illustrates the evaluation of Region-of-Interest (ROI) coders using perceptual image quality assessments. The goal of this evaluation is to characterize the coder performance by controlling the ROI quality. Perceptual measures are taken into account for evaluation since they behave as a human-made evaluation. Moreover, a perceptual assessment named Wavelet Quality Index (WQI), is introduced as another image coder evaluator. Proposed assessment aims at emulating the human vision by a weighted linear combination of three wavelet-based perceptual measures. We evaluate the following types of ROI-coders: those preserving the quality of ROI by coarse compression of background (Max-Shift coder), and those balancing the quality between ROI and background (SCM-Shift, and BbB-Shift coders). Using considered assessments for the performance evaluation of coders, results show a variation of evaluation by nature of measurement.
Keywords:Region-of-Interest  Image coding  Wavelet  Distortion measure  Quality assessment  Perceptual evaluation  Human visual system  Mean-observed scores  Rate-distortion function  IQA"}  {"#name":"keyword"  "$":{"id":"k0035"}  "$$":[{"#name":"text"  "_":"Image quality assessment  ROI"}  {"#name":"keyword"  "$":{"id":"k0045"}  "$$":[{"#name":"text"  "_":"Region of Interest  BG"}  {"#name":"keyword"  "$":{"id":"k0055"}  "$$":[{"#name":"text"  "_":"Image background  PSNR"}  {"#name":"keyword"  "$":{"id":"k0065"}  "$$":[{"#name":"text"  "_":"Peak Signal to Noise Ratio  MS-SSIM"}  {"#name":"keyword"  "$":{"id":"k0075"}  "$$":[{"#name":"text"  "_":"Multi-Scale Structural SIMilarity  VIF"}  {"#name":"keyword"  "$":{"id":"k0085"}  "$$":[{"#name":"text"  "_":"Visual Image Fidelity  MSE"}  {"#name":"keyword"  "$":{"id":"k0095"}  "$$":[{"#name":"text"  "_":"Mean Squared Error  R-Q"}  {"#name":"keyword"  "$":{"id":"k0105"}  "$$":[{"#name":"text"  "_":"Rate-Quality Function  QILV"}  {"#name":"keyword"  "$":{"id":"k0115"}  "$$":[{"#name":"text"  "_":"Quality Index based on Local Variance  RF"}  {"#name":"keyword"  "$":{"id":"k0125"}  "$$":[{"#name":"text"  "_":"Reflection Factor  VSNR"}  {"#name":"keyword"  "$":{"id":"k0135"}  "$$":[{"#name":"text"  "_":"Visual Signal to Noise Ratio  DN"}  {"#name":"keyword"  "$":{"id":"k0145"}  "$$":[{"#name":"text"  "_":"Divisive Normalization  WQI"}  {"#name":"keyword"  "$":{"id":"k0155"}  "$$":[{"#name":"text"  "_":"Wavelet-based Quality Index  Measured/Quality Value  CC"}  {"#name":"keyword"  "$":{"id":"k0175"}  "$$":[{"#name":"text"  "_":"Correlation coefficient  QND"}  {"#name":"keyword"  "$":{"id":"k0185"}  "$$":[{"#name":"text"  "_":"Quality Normalized Difference  RBD"}  {"#name":"keyword"  "$":{"id":"k0195"}  "$$":[{"#name":"text"  "_":"ROI-background difference
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