Transform Coding for On-the-Fly Learning Based Block Transforms
Block based video codecs developed in last decades have employed Discrete Cosine Transform (DCT) as a candidate transform for compacting the energy of the predicted residual data into fewer coefficients. However, DCT is not optimal for blocks with directional structure and high texture. Content-adaptive transforms can be used to achieve better compression. However, these adaptive transforms must be sent to the decoder, increasing the bit-rate in return. It is therefore important to take care of the overhead of sending the transforms in order to benefit from the better compaction of the adaptive transforms. This paper addresses this issue and provides methods to efficiently encode these adaptive transforms that are learned from the content. A statistical model is derived to determine the precision required to encode a transform with a negligible loss in the performance of adaptive transforms. Results shows that the overhead can be reduced by an average 70% for low QP and 86% for high QP values compared to encoding the complete transform with a little performance loss.