Abstract
The authors describe an efficient algorithm design for lossless video compression by using MPEG-2 as a basic research platform. Starting from MPEG motion estimation and compensation, the proposed algorithm focuses on a context tree design to fine tune the statistics and thus optimise the estimation of conditional probabilities to drive an arithmetic coder. In comparison with the existing work on context tree design, the proposed algorithm features: (i) prefix sequence matching to locate the statistics model at the internal node nearest to the stopping point, where successful match of context sequence is broken; (ii) traversing the context tree along a fixed order of context structure with a maximum number of four motion compensated errors; and (iii) context thresholding to quantise the higher end of error values into a single statistics cluster. As a result, the proposed algorithm is able to achieve competitive processing speed, low computational complexity and high compression performances, which bridges the gap between universal statistics modelling and practical compression techniques. When JPEG-LS and CALIC, the existing state-of-the-art in lossless compression of still images, are applied to those motion compensated error-frames as well as individual non-predicted frames to formulate benchmarks, the authors' experiments illustrate that the proposed algorithm outperforms JPEG-LS by up to 24% and CALIC by up to 22%, yet the processing time ranges from less than 2 s per frame to 6 s per frame on a typical PC computing platform.