Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 66 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 21 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 91 tok/s Pro
Kimi K2 202 tok/s Pro
GPT OSS 120B 468 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC (2306.13694v1)

Published 23 Jun 2023 in eess.IV

Abstract: Motion compensation is one of the key technologies enabling the high compression efficiency of modern video coding standards. To allow compression of spherical video content, special mapping functions are required to project the video to the 2D image plane. Distortions inevitably occurring in these mappings impair the performance of classical motion models. In this paper, we propose a novel motion plane adaptive motion modeling technique (MPA) for spherical video that allows to perform motion compensation on different motion planes in 3D space instead of having to work on the - in theory arbitrarily mapped - 2D image representation directly. The integration of MPA into the state-of-the-art H.266/VVC video coding standard shows average Bj{\o}ntegaard Delta rate savings of 1.72\% with a peak of 3.37\% based on PSNR and 1.55\% with a peak of 2.92\% based on WS-PSNR compared to VTM-14.2.

Citations (2)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.