Sparse Feature Fidelity for Perceptual Image Quality Assessment

Abstract

The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.

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

“…In experiments, the following 16 IQA measures with publicly available objective scores, or source-code, for used benchmarks took part in the optimisation: VSI [15], FSIM [13], FSIMc [13], GSM [19], IFC [12], IW-SSIM [10], MAD [23], MSSIM [9], NQM [6], PSNR [38], RFSIM [16], SR-SIM [14], SSIM [8], VIF [11], IFS [41], and SFF [42]. It is worth noting that MAD is a multimeasure, but it was used in the optimisation due to its popularity and availability of the source-code.…”

Flexible Actuator With Variable Stiffness and Its Decoupling Control Algorithm: Principle Prototype Design and Experimental Verification

“…In [41], MUMBLIM model based on sparsity was presented to predict objective scores. Chang et al [42] used sparse feature fidelity (SFF) to measure distortion. Shao et al [43] simulated monocular and binocular visual perception through sparse representation.…”

Naturalization of Screen Content Images for Enhanced Quality Evaluation

“…We first test the performances of the state-of-the-art FR natural image quality metrics, including MS-SSIM [8], IW-SSIM [9], VIF [10], MAD [11], FSIM [12], GSIM [13], GMSD [14], LTG [15] and SFF [16]. Table 1 summarizes the experimental results before and after incorporating the proposed naturalization module, together with a statistics of the performance gains in percentage.…”