arXiv Computer Vision By Runyu Yang, Hyomin Choi, Fabien Racap\'e, Ivan V. Baji\'c

Inter-Frame Predictive Contour Coding for Semantic Map Compression

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The paper introduces an inter‑frame predictive contour coding framework for lossless compression of dynamic semantic maps. It predicts blob positions and label indices from a reference frame, then performs contour prediction using chain‑code units from the reference contours, and selects between intra and inter coding based on estimated coding cost. Experiments on the VSPW dataset show the method outperforms existing lossless semantic map compression techniques, achieving a 3.0% average bitrate reduction without a significant increase in encoding time.

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arXiv Computer Vision
Sep 23

You've Seen Enough: Quality-Constrained Image Coding for Machines

The paper introduces Quality-Constrained Image Coding for Machines (ICM), which compresses images by treating a computer vision application as the primary observer while limiting human-observed quality to a specified target. By formulating joint compression and segmentation as a constrained optimization problem, the authors design penalty functions that steer the codec toward the desired visual quality, allowing remaining coding capacity to enhance machine performance. Experiments demonstrate significant bitrate savings—up to 22.82% over unconstrained joint optimization and 29.81% over a simple rate–distortion baseline—while maintaining target visual quality without added complexity.

By Khoa Pham-Dinh, Sanaz Nami, Hamed Rezazadegan Tavakoli, Moncef Gabbouj, Farhad Pakdaman