BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment
This work addresses the problem of inaccurate color perception modeling in image quality assessment for applications like image processing and computer vision, representing an incremental improvement by assisting existing methods.
The paper tackled the oversimplification of color perception in full-reference image quality assessment by proposing BLeSS, a biologically-inspired spatiochromatic similarity method, which enhanced the performance of existing estimators like FSIM and SR-SIM, achieving 100% improvement in color-based degradations and significant gains on the TID 2013 database.
This paper proposes a biologically-inspired low-level spatiochromatic-model-based similarity method (BLeSS) to assist full-reference image-quality estimators that originally oversimplify color perception processes. More specifically, the spatiochromatic model is based on spatial frequency, spatial orientation, and surround contrast effects. The assistant similarity method is used to complement image-quality estimators based on phase congruency, gradient magnitude, and spectral residual. The effectiveness of BLeSS is validated using FSIM, FSIMc and SR-SIM methods on LIVE, Multiply Distorted LIVE, and TID 2013 databases. In terms of Spearman correlation, BLeSS enhances the performance of all quality estimators in color-based degradations and the enhancement is at 100% for both feature- and spectral residual-based similarity methods. Moreover, BleSS significantly enhances the performance of SR-SIM and FSIM in the full TID 2013 database.