Uniform quantization error for Laplacian sources with applications to JPEG standard

In this paper we propose a novel method for computing JPEG quantization matrices based on desired mean square error, avoiding the classical trial and error procedure. First, we use a relationship between a Laplacian source and its quantization error when uniform quantization is used in order to find a model for uniform quantization error. Then we apply this model to the coefficients obtained in the JPEG standard once the image to be compressed has been transformed by the discrete cosine transform. This allows us to compress an image using JPEG standard under a global MSE constraints and a set of local constraints determined by JPEG standard and visual criteria. Simulations show that our method generates better quantization matrices than the classical method scaling the JPEG default quantization matrix, with a cost lower than the coding, decoding and error measuring procedure.