Image Quality Assessment by Dual Tree Complex Wavelet Transform In Fractal Analysis

Abstract Reduced-reference image quality assessment (RRIQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. In this paper, multifractal analysis is personalized to reduced-reference image quality assessment (RR-IQA). A new RR-QA approach is proposed, which measures the difference of spatial arrangement between the reference image and the distorted image in terms of spatial regularity measured by fractal dimension. An image is first shown in wavelet domain using duel tree complex wavelet transform. Then, fractal dimensions are computed on each wavelet sub-band and concatenated as a feature vector. Finally, the extracted features are pooled as the quality score of the distorted image using ℓ1 distance. Compared with existing methods, the proposed approach measures image quality from the perspective of the spatial distribution of image patterns. The proposed method was evaluated on seven public benchmark data sets. Experimental results have demonstrated the excellent performance of the proposed method in comparison with state-of-the-art approaches.

Keywords RR-IQA, multifractal, spatial arrangement, wavelet, sub-band, DTCWT

Click here to Download Full Paper
Translate »