Context Adaptive Extended Chain Coding for Semantic Map Compression

TL;DR

This paper introduces a novel lossless compression method for semantic maps that leverages contour topology and shared boundaries, achieving significant bitrate reduction and runtime improvements.

Contribution

It proposes an extended chain code framework with context-adaptive entropy coding and skip mechanisms for efficient semantic map compression.

Findings

Achieves 18% average bitrate reduction over state-of-the-art methods.

Encoder and decoder reduce runtime by up to 98% and 50%.

Demonstrates consistent gains on occupancy map datasets.

Abstract

Semantic maps are increasingly utilized in areas such as robotics, autonomous systems, and extended reality, motivating the investigation of efficient compression methods that preserve structured semantic information. This paper studies lossless compression of semantic maps through a novel chain-coding-based framework that explicitly exploits contour topology and shared boundaries between adjacent semantic regions. We propose an extended chain code (ECC) to represent long-range contour transitions more compactly, while retaining a legacy three-orthogonal chain code (3OT) as a fallback mode for further efficiency. To efficiently encode sequences of ECC symbols, a context-adaptive entropy coding scheme based on Markov modeling is employed. Furthermore, a skip-coding mechanism is introduced to eliminate redundant representations of shared contours between adjacent semantic regions, supporting both complete and partial skips via run-length signaling. Experimental results demonstrate that the proposed method achieves an average bitrate reduction of 18\% compared with a state-of-the-art benchmark on semantic map datasets. In addition, the proposed encoder and decoder achieve up to 98\% and 50\% runtime reduction, respectively, relative to a modern generic lossless codec. Extended evaluations on occupancy maps further confirm consistent compression gains across the majority of tested scenarios. The source code is made publicly available at \url{https://github.com/InterDigitalInc/LosslessSegmentationMapCompression}.