Self-Contained Probability Representation for Deployable Lossless Image Compression

Recent learning-based lossless image compression methods achieve strong compression, but they rely on large pre-trained models whose heavy inference cost hinders practical deployment. To this end, we introduce a compact representation of image probability which is embedded in a bitstream, shortly. The representation is compiled once into a lookup table, eliminating repeated neural inference during coding. For deployable on-device encoding, we marginalize image-specific features into a reusable table without per-image fitting, while a compact tree restores self-contained delivery. Our method achieves 95.5$\times$/52.8$\times$ faster decoding/encoding than the fastest evaluated learned baseline, and 4$\times$/5.7$\times$ faster decoding/encoding than classical codecs, while maintaining competitive rate–throughput trade-offs. Experiments demonstrated capabilities beyond prior learned codecs, including 16-bit image compression, practical encoding and decoding on devices ranging from servers to wearables, and a lossy extension through bit-plane truncation. Self-contained decoding demo files are included in the supplementary material.