5 accepted papers
Traditional image compression prioritizes pixel fidelity but often preserves details irrelevant to downstream vision tasks. Compressing task-specific representations instead better aligns with task semantics, yet redundant information persists across correlated tasks. Existing multi-task compression
Exploiting bi-directional context prediction has long been recognized as a key direction for improving compression efficiency in neural video coding. However, existing neural B-frame codecs still exhibit limited performance gains, particularly in high-resolution videos with large motion, where optic
360-degree video is a panoramic video technology designed to offer audience an immersive visual experience. In Motion Constrained Tile Set (MCTS)-based streaming schemes, the server only updates Field-Of-View (FOV) coordinates when codec generating keyframes, thus the keyframe interval significantly…
Video compression is a fundamental topic in the visual intelligence, bridging visual signal sensing/capturing and high-level visual analytics. The broad success of artificial intelligence (AI) technology has enriched the horizon of video compression into novel paradigms by leveraging end-to-end opti
Rate control (RC) is crucial in achieving stable and smooth bitrate variation in video compression and transmission. Existing RC methods for neural video compression (NVC) have made strong assumptions on solving bit allocation parameters using a pre-defined model, leading to high bit-rate errors (BR…