Multi-stage Prediction Encoding for 3D Graphics Compression
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Solution Overview
Problem
Current methods for compressing temporally varying 3D computer graphics models are inefficient, as they either require new models for each time instant or rely on specialized animation models that are not generalizable, leading to temporal redundancy and limited compression gains.
Innovation Solution
An encoding and decoding apparatus that predicts coordinate data and prediction error vectors using multiple stages of prediction, followed by processing these differences to reduce bit-rate, incorporating temporal and spatial prediction methods and binary arithmetic coding with adaptive probability estimations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple prediction stages and efficient encoding techniques are used, then compression gain is improved, but device complexity increases
Solution Approach 1:
The encoding process is divided into multiple distinct prediction stages (temporal prediction, spatial prediction, and combined prediction) and separate encoding paths (intra-encoding and inter-encoding). This segmentation allows the system to achieve high compression through multiple specialized prediction passes while maintaining modular complexity that can be managed and implemented efficiently.
Solution Approach 2:
The encoding apparatus dynamically switches between different prediction modes (intra-encoding and inter-encoding) and prediction stages based on the characteristics of the graphics model sequence. The system adapts its complexity level by selecting appropriate prediction strategies for different temporal and spatial characteristics, optimizing compression without always requiring maximum device complexity.
2Quantity of substance
If temporal prediction is used to compress graphics model sequences, then bit-rate is reduced, but prediction accuracy may deteriorate for complex movements
Solution Approach 1:
The system dynamically adapts its prediction strategy based on the movement characteristics of the graphics model. For uniform movements, simple temporal prediction suffices and achieves high compression. For complex movements with topology changes or non-uniform motion, the system switches to more sophisticated spatial prediction or combined prediction methods, maintaining accuracy while still achieving compression through selective application of prediction techniques.
Solution Approach 2:
The prediction process is segmented into multiple independent stages (temporal prediction stage, spatial prediction stage, and combined prediction stage). This allows the system to apply the appropriate level of prediction complexity to different parts of the data, achieving overall high compression while maintaining accuracy for complex movements through the complementary nature of the segmented prediction approaches.
Data Source
AI summary
Introducing a further prediction stage, namely a prediction of the motion vectors or the prediction error of the first prediction stage, does indeed at first increase the encoding or compression effort and, correspondingly, also the decoding or decompression effort, but the prediction proposed here leads to a significant improvement of the compression gain in relation to the effort in most graphics model sequences due to the uniformity of the motion.