A New Trellis Vector Residual Quantizer: Applications To Image Coding

We present a new Trellis Coded Vector Residual Quantizer (TCVRQ) that combines trellis coding and vector residual quantization. We propose new methods for computing quantization levels and experimentally analyze the performances of our TCVRQ in the case of still image coding. Experimental comparisons show that our quantizer performs better than the standard Tree and Exhaustive Search Quantizers based on the Generalized Lloyd Algorithm (GLA). 1. A NEW TRELLIS VECTOR RESIDUAL QUANTIZER Quantization is the process of approximating a continuous -amplitude signal by a digital (discrete-amplitude) signal, minimizing a distortion measure (or error). Unfortunately, fixed the coding rate R, an optimal VQ requires computational and storage resources that grow exponentially with the vector dimension. Moreover, Lin in [1] showed that the design of an optimal Vector Quantizer is an NP-complete problem. The design of sub-optimal vector quantizers is an interesting alternative to scalar quantizers f..