Abstract. The Discrete Cosine Transform (DCT)-based image compression is widely used in today's communication systems. Significant research devoted to this domain has demonstrated that the optical compression methods can offer a higher speed but suffer from bad image quality and a growing complexity. To meet the challenges of higher image quality and high speed processing, in this chapter, we present a joint system for DCT-based image compression by combining a VLSI architecture of the DCT algorithm and an efficient quantization technique. Our approach is, firstly, based on a new granularity method in order to take advantage of the adjacent pixel correlation of the input blocks and to improve the visual quality of the reconstructed image. Second, a new architecture based on the Canonical Signed Digit and a novel Common Subexpression Elimination technique is proposed to replace the constant multipliers. Finally, a reconfigurable quantization method is presented to effectively save the computational complexity. Experimental results obtained with a prototype based on FPGA implementation and comparisons with existing works corroborate the validity of the proposed optimizations in terms of power reduction, speed increase, silicon area saving and PSNR improvement.