Image Quality Assessment Using Multi-Method Fusion

Abstract

A new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is presented in this paper. The research is motivated by the observation that there is no single method that can give the best performance in all situations. To achieve MMF, we adopt a regression approach. The new MMF score is set to be the nonlinear combination of scores from multiple methods with suitable weights obtained by a training process. In order to improve the regression results further, we divide distorted images into three to five groups based on the distortion types and perform regression within each group, which is called "context-dependent MMF" (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. To further reduce the complexity of MMF, we perform algorithms to select a small subset from the candidate method set. The result is very good even if only three quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

“…In the table, the best three results for a given benchmark are written in boldface, results not reported are denoted by "-". Some measures are not benchmark independent, i.e., their authors reported evaluation results on the benchmark that took part in the development of the measure without providing results for other benchmarks, or prepared a one IQA measure for each benchmark, also without cross-benchmark tests, e.g., [24], [26], [27], [29], [30], [31], [35], [36]. Results for approaches that are not dataset independent were excluded from comparison, they are written in italics in the table.…”

“…A conditional Bayesian mixture of experts model with a support vector machines classifier was used in [30] for combining SSIM, VSNR, and VIF using k-nearest-neighbour regression. A support vector regression approach was shown in [31]. In [32], in turn, image blocks were first classified using decision trees and then FSIM [13], mean squared error, and different variations of PSNR [33] were combined.…”

Subjective Score Predictor: A New Evaluation Function of Distorted Image Quality

“…Usually, the distortion types include noise, blur, compression, transmission, and intensity deviation, which are involved in the open IQA datasets [24]. Their parameters are standard deviation of noise N , standard deviation of blur kernel B , bit rate of per pixel BPP , transmission signal-to-noise ratio SNR , and intensity change value V I , respectively.…”

Visual quality assessment: recent developments, coding applications and future trends

“…MAD [48] and MMF [57,59] are representatives for multiple strategies and MMF, respectively. Especially for the latter one, appropriate fusion of existing metrics opens the chances to build on the strength of each participating metric and the resultant framework can be even used when new, good metrics emerge.…”