ChronoSC: Task-Oriented Semantic Communication via Temporal-to-Color Encoding

2026-05-19 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Robotics & Autonomous Systems · Depth: Expert, long

Summary

ChronoSC is a novel task-oriented semantic communication framework designed for Video Question Answering (VideoQA) in low-resource edge deployments. It introduces Chrono-Color Stacking, a lightweight, lossless projection scheme that encodes temporal video dynamics into a single static image, achieving extreme temporal compression. This compact representation is transmitted via a Deep Joint Source-Channel Coding (DeepJSCC) transceiver called Motion-Aware Swin Transceiver (MAST), which explicitly reconstructs a pixel-domain semantic image at the receiver. This enables direct reuse of pre-trained vision-language models like BLIP for inference from noisy chrono-images. Experiments on the CLEVRER dataset demonstrate ChronoSC achieves up to 192 times bandwidth reduction compared to raw video transmission, maintains 76.2% VideoQA accuracy at 0 dB SNR, and reduces computational complexity by 41.8 times compared to 3D CNNs.

Key takeaway

For Computer Vision Engineers developing edge-based video analytics, ChronoSC offers a compelling approach to overcome bandwidth and latency constraints. Its Chrono-Color Stacking and Motion-Aware Swin Transceiver (MAST) enable significant data reduction (192x) and robust performance under noisy conditions, making it suitable for resource-constrained IoT or UAV deployments. You should consider integrating this temporal-to-color encoding strategy to leverage existing vision-language models for efficient, task-specific video understanding.

Key insights

ChronoSC enables efficient VideoQA by encoding video temporal dynamics into a single static image for extreme compression and robust transmission.

Principles

• Task-oriented communication prioritizes relevant information over raw data.
• Temporal dynamics can be chromatically encoded into a static image.
• Decoupled training allows reuse of pre-trained foundation models.

Method

ChronoSC uses Chrono-Color Stacking (background subtraction, hue shifting, max projection) to create a semantic image, transmitted by a motion-aware DeepJSCC (MAST) transceiver, and then processed by a fine-tuned BLIP model for VQA.

In practice

• Encode temporal video data into a single RGB image.
• Prioritize dynamic regions for robust wireless transmission.
• Fine-tune pre-trained VLMs on chromatically encoded images.

Topics

• ChronoSC
• Semantic Communication
• Chrono-Color Stacking
• Video Question Answering
• Deep Joint Source-Channel Coding

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.