π€ AI Summary
This study presents the first empirical evaluation of entropy-conserving binarization (ECB) within a real-world CABAC framework, assessing both compression efficiency and computational overhead. Using a custom M-coder-based CABAC encoder, the authors integrated ECB alongside UEG, single-context Huffman, and HuffmanPos, conducting 2,480 bit-exact round-trip tests across synthetic data, procedurally generated images, and the Kodak dataset. Results demonstrate that ECB consistently outperforms single-context Huffman across all quantization parameters, achieving rate savings of 0.031β0.113 bits per symbol, while HuffmanPos surpasses other methods in 12 out of 15 source units. The primary driver of rate differences is attributed to context assignment rather than binarization length. To address ECBβs 7β10Γ higher decoding latency, the work proposes a single-pass interleaved decoding scheme to mitigate delay.
π Abstract
CABAC, the entropy coder of H.264/AVC and the basis for HEVC and VVC, decomposes multi-symbol values into bins via a binarization scheme before a binary arithmetic coder. H.264 uses Truncated Unary plus k-th order Exp-Golomb (UEG); alternatives include canonical Huffman and the entropy-conserving binarization (ECB), which provably preserves entropy mapping m-ary data to m-1 binary strings but has not been evaluated inside a production binary arithmetic coder. We integrate ECB into a from-scratch CABAC implementation alongside UEG, single-context Huffman, and a Huffman variant with per-bin-position contexts (HuffmanPos), all sharing one M-coder backend. We benchmark all four on synthetic sources, DCT residuals from a procedural image, and the full 24-image Kodak suite (2,480 round-trip trials, bit-exact verified). On the procedural image, a sparsity-driven crossover at Q=8 lets ECB overtake single-context Huffman, reaching 27 percentage points below at Q=32. On Kodak the crossover shifts below the tested range and ECB beats single-context Huffman at every Q, the gap growing from 0.031 to 0.113 bits per symbol. HuffmanPos, sharing Huffman's codewords but allocating one context per bin position, beats ECB on 12 of 15 source cells and loses by at most 0.56 percentage points on the other three, despite the same per-symbol bin count as single-context Huffman. This isolates the dominant mechanism: at low source entropy the rate gap is driven primarily by context allocation over the bin stream, not the binarization's per-symbol bin count. ECB's rate efficiency costs 7 to 10x in decoder latency on large alphabets, traced to an O(N*m) decoder; we sketch an interleaved single-pass variant that would close this gap. Code, benchmarks, and raw data are open source.