Retinally reconstructed images: digital images having a resolution match with the human eye

Current digital image/video storage, transmission and display technologies use uniformly sampled images. On the other hand, the human retina has a nonuniform sampling density that decreases dramatically as the solid angle from the visual fixation axis increases. Therefore, there is sampling mismatch between the uniformly sampled digital images and the retina. This paper introduces retinally reconstructed images (RRI’s), a novel representation of digital images, that enables a resolution match with the human retina. To create an RRI, the size of the input image, the viewing distance and the fixation point should be known. In the RRI coding phase, we compute the “retinal codes,” which consist of the retinal sampling locations onto which the input image projects, together with the retinal outputs at these locations. In the decoding phase, we use the backprojection of the retinal codes onto the input image grid as B-Spline control coefficients, in order to construct a three-dimensional (3-D) B-spline surface with nonuniform resolution properties. An RRI is then created by mapping the B-spline surface onto a uniform grid, using triangulation. Transmitting or storing the “retinal codes” instead of the full resolution images enables up to two orders of magnitude data compression, depending on the resolution of the input image, the size of the input image and the viewing distance. The data reduction capability of retinal codes and RRI is promising for digital video storage and transmission applications. However, the computational burden can be substantial in the decoding phase.