GPU Motion Estimation via Quadratic Surface Modeling
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Solution Overview
Problem
Conventional solutions require multiple computing platforms to handle both computer-generated graphics and video encoding/decoding, lacking a single platform that can efficiently perform both tasks.
Innovation Solution
A graphics processing unit (GPU) is utilized to perform improved motion estimation by determining motion vectors with integral and fractional resolution through a method involving local area exhaustive searches and quadratic surface modeling, enabling efficient video encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional solutions use multiple computing platforms (CPU with ASICs) for video encoding/decoding, then video processing capability is achieved, but system complexity and cost increase
Solution Approach 1:
The GPU is designed to perform both traditional graphics rendering and video encoding/decoding operations. The same parallel processing architecture that renders 3D graphics is leveraged to execute motion estimation, motion compensation, and other video processing tasks, eliminating the need for separate dedicated video processing hardware and reducing overall system complexity
Solution Approach 2:
The patent combines graphics processing and video processing functions into a single GPU platform. By merging these previously separate functions, the system achieves both video processing capability and reduced hardware complexity through unified resource utilization
2Measurement precision
If exhaustive search is performed for motion estimation, then matching accuracy is improved, but computational time increases
Solution Approach 1:
The exhaustive search process is segmented into two distinct phases: a coarse search at integer pixel resolution to identify a good match prediction block, followed by a refined local area exhaustive search centered around that block to achieve the final best match. This segmentation reduces the overall search space and computational time while maintaining accuracy
Solution Approach 2:
The coarse search phase performs preliminary action by identifying a good match prediction block before the refined search begins. This preliminary identification narrows down the search area for the subsequent detailed search, preventing a complete exhaustive search across the entire frame and significantly reducing computational time
3Productivity
If integer pixel resolution motion estimation is used, then computational speed is improved, but matching precision deteriorates
Solution Approach 1:
The patent implements a dynamic two-stage resolution approach where the search initially operates at integer pixel resolution for speed, then transitions to fractional pixel resolution (e.g., half-pixel or quarter-pixel) for the refined local search. This dynamic adjustment of precision levels optimizes both computational speed and matching precision
Solution Approach 2:
The refined search applies higher precision (fractional pixel resolution) only in the local area around the good match prediction block identified in the coarse search, rather than applying high precision throughout the entire search space. This local quality enhancement maintains accuracy where needed while preserving computational efficiency in broader areas
Data Source
AI summary
The embodiments disclosed herein provide systems and methods for improved motion estimation using a graphics processing unit. One such embodiment includes a method for determining a motion vector describing motion relative to a reference block, which comprises determining which of a plurality of prediction blocks is a good match with the reference block, according to a match criteria. The method further comprises performing a local area exhaustive search, in an area centered around the good match prediction block, to produce a best match with the reference block. The best match has integral pixel resolution. The method further comprises modeling the degree of match between the best match and the reference block as a quadratic surface and analytically determining a minima of the quadratic surface, corresponding to a best matching block with fractional resolution. The method further comprises computing a fractional motion vector based on the best matching block with fractional resolution.