Automatic and optimal hierarchical quantizer decomposition to build knowledge for video transmission

Abstract

Current video encoding methods base their decisions (which sequence of bits must be sent at each instant t) on a single knowledge base throughout all transmission times (in most cases, the knowledge is based on energy values, so coefficients with higher energy are prioritized over to those with lower energy). This way, there are no mechanisms working simultaneously with, or in parallel with, the transmission process and imposing the need to modify the knowledge base in accordance with the requirements of the transmission process (sending the information that will produce the best possible quality per bit transmitted). Since the knowledge base is conceived statically (it does not change over time), there will come a time when all information to be transmitted is of equal relevance, even though there may still be differences in that information. Based on this reasoning, we propose a video compression method with automatic internal mechanisms that make it possible to specify a knowledge base (containing the optimal sequences to be sent for each quantizer) at each instant of the transmission process. The methodology is based on a hierarchical quantizer decomposition. In the first level we have quantizers with an high linear velocity, and in the second level, quantizers with high energy. We automatically select the best decomposition by minimizing the cost of coding the information. Comparisons with the state of the art in video coding show the advantages of the approach.