Asymptotic Closed-Loop Design Of Transform Modes For The Inter-Prediction Residual In Video Coding

Abstract

Transform coding is a key component of video coders, tasked with spatial decorrelation of the prediction residual. There is growing interest in adapting the transform to local statistics of the inter-prediction residual, going beyond a few standard trigonometric transforms. However, the joint design of multiple transform modes is highly challenging due to critical stability problems inherent to feedback through the codec’s prediction loop, wherein training updates inadvertently impact the signal statistics the transform ultimately operates on, and are often counter-productive (and sometimes catastrophic). It is the premise of this work that a truly effective switched transform design procedure must account for and circumvent this shortcoming. We introduce a data-driven approach to design optimal transform modes for adaptive switching by the encoder. Most importantly, to overcome the critical stability issues, the approach is derived within an asymptotic closed loop (ACL) design framework, wherein each iteration operates in an effective open loop, and is thus inherently stable, but with a subterfuge that ensures that, asymptotically, the design approaches closed loop operation, as required for the ultimate coder operation. Experimental results demonstrate the efficacy of the proposed optimization paradigm which yields significant performance gains over the state-of-the-art.