Sparse representation of texture patches for low bit-rate image compression

Abstract

This paper proposes a sparse representation based approach for low bit-rate image compression using the learnt over-complete dictionary of texture patches. We first propose to compress each patch of the image with sparse and compressible linear combinations (via nonzero coefficients) of texture patterns encoded in a dictionary for image patches. Then, we find out that the compressibility and sparsity of coefficients can be achieved by the proposed recursive procedure of solving ℓ1 optimization problem of sparse representation. Moreover, rather than transform-based patterns (e.g. DCT), we explore the basic texture patterns from other training images with a learning algorithm based on the gradient descent, to form the over-complete dictionary. The experimental results demonstrate the effectiveness of the proposed approach.