HVSweighted

HVSweighted is a term used in image processing to refer to weighting schemes that encode the sensitivity of the human visual system (HVS) into the analysis or processing of visual data. In practice, HVSweighted methods assign a weight to each pixel or coefficient so that errors or allocation of bits align with perceptual importance rather than purely numerical error. The goal is to yield higher perceptual quality with the same resources by emphasizing regions where humans are more likely to notice artifacts.

Construction of HVSweighted weights draws on models of the human visual system, particularly the contrast sensitivity function (CSF) that describes sensitivity to spatial frequencies, as well as luminance adaptation and color sensitivity. Weights can be space-variant or transform-domain; methods include applying CSF-based masks in the spatial domain or weighting transform coefficients with a CSF-derived mask, and sometimes using foveated weighting based on gaze location. Additional cues such as local texture, edge strength, or gradient magnitude may be incorporated to better reflect perceptual saliency.

Applications of HVSweighted techniques include perceptual image and video compression, objective quality assessment, denoising, super-resolution, and streaming. By aligning distortion measures and rate-distortion optimization with perceptual impact, HVSweighted approaches can achieve comparable or better perceived quality with different resource budgets. They are primarily discussed in research and development contexts and are not standardized as a single algorithm, with various implementations and domain-specific adaptations.

In relation to the broader field, HVSweighted sits alongside perceptual weighting and perceptual error weighting, emphasizing the role of human vision models in guiding processing choices.