Asymptotically Optimal Scalable Coding for Minimum Weighted Mean Square Error
DCC '01 Proceedings of the Data Compression Conference
Receiver only optimized semi-hard decision VQ for noisy channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
The high-resolution rate-distortion function under the structural similarity index
EURASIP Journal on Advances in Signal Processing - Special issue on theory and application of general linear image processing
| Hi-index | 754.84 |
Gersho's (1979) bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki, and Gray (1980) generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the distortion is uniquely determined by the vector quantization error, i.e., the Euclidean difference between the original vector and the codeword into which it is quantized. We generalize these asymptotic bounds to input-weighted quadratic distortion measures and measures that are approximately output-weighted-quadratic when the distortion is small, a class of distortion measures often claimed to be perceptually meaningful. An approximation of the asymptotic distortion based on Gersho's conjecture is derived as well. We also consider the problem of source mismatch, where the quantizer is designed using a probability density different from the true source density. The resulting asymptotic performance in terms of distortion increase in decibels is shown to be linear in the relative entropy between the true and estimated probability densities