Video Compression Buffer Management for Shifting Camera Views
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Solution Overview
Problem
Video compression technologies face increased bit rate when encoding 3D scenes due to shifting camera views, leading to inefficient use of inter-frame prediction and higher bit rates for maintaining video quality.
Innovation Solution
The method involves dividing a viewing area into sections based on azimuth and elevation coordinates, storing reference frames in a long-term buffer to optimize coverage, and selecting frames for inter-frame coding from either the long-term or short-term buffer based on these coordinates, using sensors and a GPU to determine storage locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reference frames are stored in a long-term buffer to maintain coverage during camera view shifts, then inter-frame prediction efficiency is improved, but buffer management complexity increases
Solution Approach 1:
The viewing area is divided into multiple sections based on azimuth and elevation coordinates. Reference frames are stored in the long-term buffer organized by these coordinate sections, allowing efficient retrieval and management. This segmentation enables the system to maintain effective reference frames during camera view shifts while simplifying buffer management through structured organization.
2Productivity
If the decoder buffer is used to store previously decoded frames for inter-frame prediction, then video compression efficiency is improved, but bit rate increases when camera views shift
Solution Approach 1:
The system pre-organizes reference frames in the long-term buffer by azimuth and elevation coordinates before they are needed for prediction. This preliminary organization allows the decoder to efficiently retrieve appropriate reference frames even when camera views shift, maintaining compression efficiency without requiring higher bit rates for re-encoding.
Solution Approach 2:
The system uses feedback from motion vectors and coordinate information to dynamically select appropriate reference frames from the long-term buffer for inter-frame prediction. This feedback mechanism ensures that the correct reference frames are retrieved and used, maintaining compression efficiency while adapting to camera view changes without increasing bit rate.
3Ease of operation
If motion vectors are used to describe position differences between frames, then inter-frame prediction is enabled, but prediction accuracy decreases when camera field of view changes
Solution Approach 1:
The system divides the viewing area into local sections based on azimuth and elevation coordinates. Reference frames are organized and retrieved based on these local coordinate regions, allowing the system to maintain prediction accuracy even when the camera field of view changes. This local organization ensures that appropriate reference frames are selected for each region, preserving prediction quality.
Data Source
AI summary
A method for long-term reference prediction video compression, includes: dividing a viewing area into a plurality of viewing sections; measuring an elevation and an azimuth of a field of view (fov) in one or more of the viewing sections; determining a storage location of a video frame associated with the field of view based on the measured elevation and azimuth. A long-term reference prediction video compression encoder, includes: a buffer for storing long-term video frames; a detector to measure an elevation and an azimuth of a field of view associated with a video frame; a buffer manager to determine, based on the measured elevation and azimuth, to store data associated with the video frame in the long-term frame buffer.