PerSIM: Multi-resolution Image Quality Assessment in the Perceptually Uniform Color Domain

Abstract

An average observer perceives the world in color instead of black and white. Moreover, the visual system focuses on structures and segments instead of individual pixels. Based on these observations, we propose a full reference objective image quality metric modeling visual system characteristics and chroma similarity in the perceptually uniform color domain (Lab). Laplacian of Gaussian features are obtained in the L channel to model the retinal ganglion cells in human visual system and color similarity is calculated over the a and b channels. In the proposed perceptual similarity index (PerSIM), a multi-resolution approach is followed to mimic the hierarchical nature of human visual system. LIVE and TID2013 databases are used in the validation and PerSIM outperforms all the compared metrics in the overall databases in terms of ranking, monotonic behavior and linearity.

5 pages, 1 figure, 3 tables

Cited by in corpus (12)

• UNIQUE: Unsupervised Image Quality Estimation
• Perceptual Image Quality Assessment through Spectral Analysis of Error Representations
• CSV: Image Quality Assessment Based on Color, Structure, and Visual System
• Content-adaptive non-parametric texture similarity measure
• A combined full-reference image quality assessment approach based on convolutional activation maps
• BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment
• A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment
• A comprehensive evaluation of full-reference image quality assessment algorithms on KADID-10k
• Boosting in Image Quality Assessment
• Distorted Representation Space Characterization Through Backpropagated Gradients
• Empirical evaluation of full-reference image quality metrics on MDID database
• Semantically Interpretable and Controllable Filter Sets