Vision-model-based image foveation and motion estimation

Foveated imaging systems applicable in various single-user displays mimic the visual system's image structure, where resolution decreases gradually away from the fovea. The main benefit is the low average image resolution while maintaining high resolution at the center of the gaze. When the end user is a human observer, it is advantageous for the foveation process to closely match the visual system parameters. This work directly applies a multichannel model of the visual system to form foveated images. A systems-engineering approach applied to the vision model produces quantitative image spectral content across the visual channels. Foveated images are constructed according to the contrast threshold and image content calculated at different eccentricities. Also, variable-resolution feature detection (edge and bar) that corresponds to early visual processing is produced, based on the available image content across the channels. Motion between shifted foveated images (required in applications such as image compression and motion compensation) is estimated using either the foveated images or the detected feature images. Results using several similarity metrics and imaging conditions show that reliable motion estimation can be achieved, while features with nonsimilar resolutions (different scales) are matched.