Image compression with adaptive local cosines: a comparative study

The goal of this work is twofold. First, we demonstrate that an advantage can be gained by using local cosine bases over wavelets to encode images that contain periodic textures. We designed a coder that outperforms one of the best wavelet coders on a large number of images. The coder finds the optimal segmentation of the image in terms of local cosine bases. The coefficients are encoded using a scalar quantizer optimized for Laplacian distributions. This new coder constitutes the first concrete contribution of the paper. Second, we used our coder to perform an extensive comparison of several optimized bells in terms of rate-distortion and visual quality for a large collection of images. This study provides for the first time a rigorous evaluation in realistic conditions of these bells. Our experiments show that bells that are designed to reproduce exactly polynomials of degree 1 resulted in the worst performance in terms of the PSNR. However, a visual inspection of the compressed images indicates that these bells often provide reconstructed images with very few visual artifacts, even at low bit rates. The bell with the most narrow Fourier transform gave the best results in terms of the PSNR on most images. This bell tends however to create annoying visual artifacts in very smooth regions at low bit rate.