Peano scanning based classified vector quantiser

摘要

The authors present a classified vector quantiser (CVQ) based on Peano scanning. The Peano scanning, which is used to reduce the dimensionality of the data, provides a one-dimensional algorithm to classify an image block. The class of the block is determined based on its Peano scanning value from a look up table (LUT) of representative Peano scanning values and their associated classes. The Peano scanning algorithm is easily implemented in hardware, and the class can be determined in a logarithmic time proportional to the number of entries in the LUT when using a binary search algorithm on the sorted version of the LUT. Moreover, the class lookup table is easily implemented in real time. An effective algorithm is used to generate all the codebooks of the classes simultaneously in a systematic way by growing a greedy tree for each class in an interconnected way. The monochromatic images encoded in the range of 0.625 approximately 0.813 bits/pixel, with a 16-dimensional vector size, are shown to preserve the edge integrity and quality as determined by subjective and objective measures.