In Full-Reference Image Quality Assessment (FR-IQA), subjective Mean Opinion Scores (MOS) reflect human retinal perception, whereas objective metrics operate on the displayed image. Bridging these domains requires parametric mappings that are sensitive to viewing distance. This paper introduces Blur-Equivalent Linearized Estimator (BELE), a lightweight and perceptually interpretable FR-IQA model that disentangles the impact of strong edge degradations from that of texture distortions. BELE computes two indices: a blur index derived from a linearized estimator of Positional Fisher Information loss on strong edges, explicitly accounting for viewing distance; and a texture index based on a Complex Peak Signal-to-Noise Ratio (CPSNR) that captures distortions affecting fine spatial details in textured regions. All distortion estimates are combined via low-order polynomial fitting, with a focalization term applied in this final stage to replace the VQEG rectification, thereby eliminating its limitations such as overfitting and lack of interpretability. BELE is entirely training-free, requiring only five interpretable parameters, and achieves very low computational complexity. Extensive experiments on six benchmark datasets demonstrate that BELE attains competitive or superior correlation with MOS compared to both classical and deep learning-based FR-IQA methods, while offering strong generalization, real-time feasibility, and minimal resource demands.

Objective Image Quality Assessment (IQA) methods often lack of linearity of their quality estimates with respect to scores expressed by human subjects and therefore IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training presents a challenge in terms of generalization hampering result comparison across different applications and operative conditions. In this paper, new Full Reference (FR) techniques, providing estimates linearly correlated with human scores without using calibration are introduced. We show that on natural images, application of estimation theory and psychophysical principles to images degraded by Gaussian blur leads to a so-called canonical IQA method, whose estimates are linearly correlated to both the subjective scores and the viewing distance. Then, we show that any mainstream IQA methods can be reconducted to the canonical method by converting its metric based on a unique specimen image. The proposed scheme is extended to wide classes of degraded images, e.g. noisy and compressed images. The resulting calibration-free FR IQA methods allows for comparability and interoperability across different imaging systems and on different viewing distances. A comparison of their statistical performance with respect to state-of-the-art calibration prone methods is finally provided, showing that the presented model is a valid alternative to the final 5-parameter calibration step of IQA methods, and the two parameters of the model have a clear operational meaning and are simply determined in practical applications. The enhanced performance are achieved across multiple viewing distance databases by independently realigning the blur values associated with each distance. • The work provides a novel model for the realistic estimation of the visual discomfort caused by blur of natural scenes, considering the cognitive needs of the subjects and the adaption capability of the visual system. • We i) present the canonical estimator useful for a-priori linearization of the blur metric and ii) show how any classical IQA metric can be mapped into an equivalent blur metric. • We present a 2-parameter model as an effective and fast alternative to the 5-parameter scoring function suggested by VQEG, in the final step of the study of Full Reference IQA methods.