In this correspondence, we propose to add a lossless compression
functionality into existing MPEGs by developing a new context tree
to drive arithmetic coding for lossless video compression. In comparison
with the existing work on context tree design, the proposed algorithm features
in 1) 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; 2) traversing the context tree along a fixed order of
context structure with a maximum number of four motion compensated errors;
and 3) context thresholding to quantize 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 modeling and practical compression techniques. Extensive experiments
show 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 seconds per frame to 6 seconds per frame on a typical PC computing
platform.
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