  Stereo image quality assessment (SIQA) plays a crucial role in evaluating and
improving the visual experience of 3D content. Existing visual properties-based
methods for SIQA have achieved promising performance. However, these approaches
ignore the top-down philosophy, leading to a lack of a comprehensive grasp of
the human visual system (HVS) and SIQA. This paper presents a novel Stereo
AttenTion Network (SATNet), which employs a top-down perspective to guide the
quality assessment process. Specifically, our generalized Stereo AttenTion
(SAT) structure adapts components and input/output for stereo scenarios. It
leverages the fusion-generated attention map as a higher-level binocular
modulator to influence two lower-level monocular features, allowing progressive
recalibration of both throughout the pipeline. Additionally, we introduce an
Energy Coefficient (EC) to flexibly tune the magnitude of binocular response,
accounting for the fact that binocular responses in the primate primary visual
cortex are less than the sum of monocular responses. To extract the most
discriminative quality information from the summation and subtraction of the
two branches of monocular features, we utilize a dual-pooling strategy that
applies min-pooling and max-pooling operations to the respective branches.
Experimental results highlight the superiority of our top-down method in
advancing the state-of-the-art in the SIQA field. The code is available at
https://github.com/Fanning-Zhang/SATNet.
