Two-Dimensional Approach to Full-Reference Image Quality Assessment Based on Positional Structural Information

Abstract

A method for full-reference visual quality assessment based on the 2-D combination of two diverse metrics is described. The first metric is a measure of structural information loss based on the Fisher information about the position of the structures in the observed images. The second metric acts as a categorical indicator of the type of distortion that images underwent. These two metrics constitute the inner state of a virtual cognitive model, viewed as a system whose output is the automatic visual quality estimate. The use of a 2-D metric fills the intrinsic incompleteness of methods based on a single metric while providing consistent response across different image impairment factors and blind distortion classification capability with a modest computational overhead. The high accuracy and robustness of the method are demonstrated through cross-validation experiments.

A Detail-Based Method for Linear Full Reference Image Quality Prediction

“…One typical problem of most FR IQA methods is their unequal sensitivity with respect to the covered impairments [16], [17]. To compensate for this drawback, some methods combine values of the same metric calculated at different resolutions [13], relying on the fact that blur effects vanish when resolution shrinks, whereas noise and artifacts are only attenuated.…”

“…This objective was also pursued in [20] for video. In [17] a categorical index was paired to the positional Fisher information. Recently, a similar approach was followed in [27].…”

“…Another issue overlooked by most existing IQA methods is that the relationship between metrics and the empirical DMOS scale is strongly non-linear [17], [28]. As a matter of fact, the Spearman Rank Order Correlation Coefficient (SROCC) [29], often employed to compare the performance of different metrics, is insensitive to linearity issues.…”

“…To this purpose, the contributions to the overall perceptual impact coming from detail losses are quantified by the loss of the detail positional information [41], [17], proportional to the square root of the gradient energy attenuation into a detail window. The perceptual impact of spurious details is quantified by a logarithmic measure of the ratio between the original gradient energy and the residual gradient energy, pooled over the image.…”

“…The Visual Information (VIF) method measures the loss of Shannon mutual information [16], while the Virtual Cognitive Method (VICOM) measures the loss of the Fisher information about the localization of patterns [17].…”