A new method to improve the precision of image quality assessment metrics: Piecewise linearization of the relationship between the metrics and mean opinion scores

Measuring the perceptual visual quality is an important task for many image and video processing applications. Although, the most accurate results are obtained through subjective evaluation, the process is quite time-consuming. To ease the process, many image quality assessment (IQA) algorithms are designed using different approaches to account for various aspects of the human visual system (HVS) over the years. Evaluating the performance of these algorithms typically involves comparison of their scores to subjective scores using Pearson Linear Correlation Coefficient (PLCC). However, because the relationship between objective and subjective scores is often inherently nonlinear, applying a nonlinear mapping, most commonly the 5-parameter logistic function proposed by Video Quality Experts Group (VQEG), prior to performance evaluation is a standard practice in the literature. In this paper, we propose a novel piecewise linearization scheme as an alternative to the widely used nonlinear mapping function. Our method employs a data dependent piecewise linear mapping to align objective metric scores with subjective quality scores, which is applicable to many different IQA metrics. We validate the effectiveness of the proposed method on three publicly available datasets (CSIQ, TID2008, TID2013) and seven different IQA metrics, using PLCC as the primary performance indicator. Experimental results show that our linearization method effectively scales metric scores and achieves stronger correlations with subjective scores yielding a higher prediction accuracy. Code to reproduce our results is publicly available at github.com/cemremuge/PiecewiseLinearization.