On entropy-constrained residual vector quantization design

Summary form only given. Entropy-constrained residual vector quantization (EC-RVQ) has been shown to be a competitive compression technique. Its design procedure is an iterative process which typically consists of three steps: encoder update, decoder update, and entropy coder update. We propose a new algorithm for the EC-RVQ design. The main features of our algorithm are: (i) in the encoder update step, we propose a variation of the exhaustive search encoder that significantly speeds up encoding at no expense in terms of the rate-distortion performance; (ii) in the decoder update step, we propose a new method that simultaneously updates the codebooks of all stages; the method is to form and solve a certain least square problem and we show that both tasks can be done very efficiently; (iii) the Lagrangian of rate-distortion decreases at every step and thus this guarantees the convergence of the algorithm. We have performed some preliminary numerical experiments to test the proposed algorithm. Both random sources and still images are considered. For random sources, the size of training sequence is 2500 and the vector size is 4. For still images, the training set consists of monochrome images from the USC database and the vector size is 4×4