Context Adaptive Extended Chain Coding for Semantic Map Compression

A team of researchers has published a new paper, 'Context Adaptive Extended Chain Coding for Semantic Map Compression,' introducing a novel framework for efficiently compressing semantic maps. These maps, which label objects and regions with semantic meaning (like 'road,' 'building,' or 'vegetation'), are critical for robotics, autonomous vehicles, and extended reality (XR). The proposed method specifically targets lossless compression, meaning no semantic information is discarded, and focuses on exploiting the structured nature of these maps by analyzing contour topology and shared boundaries between adjacent labeled regions. This addresses a growing need as these high-information-density maps become more prevalent in advanced AI systems.

The technical core of the method is an Extended Chain Code (ECC) that represents long-range contour transitions more compactly than traditional approaches, with a fallback to a legacy three-orthogonal chain code for efficiency. To encode the ECC symbols, the team employs a context-adaptive entropy coding scheme based on Markov modeling. A key innovation is a skip-coding mechanism that eliminates redundant representations of contours shared between regions, using run-length signaling. Experimental results show the method achieves an average bitrate reduction of 18% compared to a state-of-the-art benchmark. Furthermore, it offers massive runtime improvements, with the encoder seeing up to a 98% reduction and the decoder a 50% reduction compared to a modern generic lossless codec. These gains in both size and speed promise to significantly enhance the performance and feasibility of real-time systems relying on detailed semantic mapping.