Neuro-Inspired Compression of RGB Images

During the last decade, there is an ever increasing interest about the decryption and analysis of the human visual system, which offers an intelligent mechanism for capturing and transforming the visual stimulus into a very dense and informative code of spikes. The compression capacity of the visual system is beyond the latest image and video compression standards, motivating the image processing community to investigate whether a neuro-inspired system, that performs according to the visual system, could outperform the state-of the-art image compression methods. Inspired by neuroscience models, this paper proposes for a first time a neuro-inspired compression method for RGB images. Specifically, each color channel is processed by a retina-inspired filter combined with a compression scheme based on spikes. We demonstrate that, even for a very small number of bits per pixel (bpp), our proposed compression system is capable of extracting faithful and exact knowledge from the input scene, compared against the JPEG that generates strong artifacts. To evaluate the performance of the proposed algorithm we use Full-Reference (FR) and No-Reference (NR) Image Quality Assessments (IQA). We further validate the performance improvements by applying an edge detector on the decompressed images, illustrating that contour extraction is much more precise for the images compressed via our neuro-inspired algorithm.