CADeTT: Context-Adaptive Deep-Trinary-Tree Lossless Compression of Event Camera Frames

Ionut Schiopu, Radu Ciprian Bilcu

Abstract: The letter proposes an efficient context-adaptive lossless compression method for encoding event frame sequences. A first contribution proposes the use of a deep-ternary-tree of the current pixel position context as the context-tree model selector. The arithmetic codec encodes each trinary symbol using the probability distribution of the associated context-tree-leaf model. Another contribution proposes a novel context design based on several frames, where the context order controls the codec's complexity. Another contribution proposes a model search procedure to replace the context-tree prune-and-encode strategy by searching for the closest “mature” context model between lower-order context-tree models. The experimental evaluation shows that the proposed method provides an improved coding performance of 34.34% and a smaller runtime of up to $5.18\times$ compared with state-of-the-art lossless image codec FLIF and, respectively, 6.95% and $14.42\times$ compared with our prior work.