Random projection and orthonormality for lossy image compression

There exist many lossy image compression techniques, some of which are based on dimensionality reduction. In this paper, a method for lossy image compression is introduced which utilizes the dimensionality reduction technique known as Random Projection. Random Projection has proven itself as an effective technique for reducing the dimensionality of data, particularly when dimensionality d is moderately high (e.g., d < 1500). Image columns or rows are treated as vectors in feature space which are thereby reduced in size to a user specified dimension k where k ≪ d. The condition of orthonormality is utilized thereby establishing a technique applicable to image compression. Although the compression is lossy, experiments indicate that the recovered image is effectively restored. Visual data is shown in the form of comparison between original and recovered image. Quantitative data includes the compression ratio achieved, the peak signal-to-noise ratio, and the root mean square error.