Region-of-interest coding based on fovea and hierarchical trees

Abstract

Image and video compression exploits the redundancy of data to create a smaller representation. Lossy compression can be considered to be a type of transform coding where the raw data is transformed to a domain. Such a transform coding stores most of image energy into very few coefficients. In this paper we propose a compression algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) that exploits the Human Visual System (HVS) and its fovea. In order to increase the image quality of the reconstructed image, regions of interest (ROI) are defined around a given point of gaze. The use of a fovea combined with ROI for image compression can help to improve the quality of the perception of the image and preserve different levels of detail around the ROI. In the proposed approach, the image is compressed using the Lifting Wavelet Transform and then quantized at multiple compression ratios around the point of fixation of an observer, taking advantage of the natural aliasing of the HVS around the fovea. Such a compression delivers better image or frame reconstruction when a fixation point of an observer is given.