Distortion-Independent Pairwise Underwater Image Perceptual Quality Comparison

Ranking underwater images according to their quality is a key indicator comparing the performance of different methodologies and therefore is critical in the field of instrumentation and measurement. Perceiving differences in the quality of underwater images is a challenging task that has received relatively less attention, primarily due to mixed distortion, diversified image content, and the absence of high-quality reference images. Thus, based on the pairwise underwater image quality voting data, we develop a novel ternary classification transformer to predict a quality comparison of underwater images without reference. This is the first attempt to model the quality discrimination of an image pair. The proposed model combines the perception of convolutional neural networks and transformer encoder to explore local quality features and visual perceptual connections between different patch tokens. Experimental results reveal that the proposed pairwise underwater image quality comparison (PUIQC) scheme predicts noticeable quality differences correlating well with subjective perception. The quantification of complex distortions in underwater images compared to other learning-based methods is a compelling feature of this technology. It delivers competitive results in ranking the different enhancement outputs. In addition, we reveal the self-attention of local quality features within the two images and capture their responsive contribution to the quality decision, which explains the underlying subjective quality-sensitive mechanism during image quality comparison.