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

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.