Scalable Motion Estimation via Dynamic Complexity Control
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Solution Overview
Problem
Conventional video encoders face significant computational challenges in motion estimation due to varying complexity requirements across different inputs and partition types, particularly in hardware implementations like the H.264 encoder, where fast motion estimation must be repeated for every partition type and reference picture combination.
Innovation Solution
A scalable method for motion estimation is introduced, where a target complexity is set for each macroblock, and motion estimation is performed in stages across different partition types and reference frames, terminating when the target complexity is met, allowing for efficient processing and adaptation to varying hardware constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fast motion estimation techniques are used to reduce computational complexity, then processing speed is improved, but implementation difficulty increases due to varying complexity requirements for different inputs and partition types
Solution Approach 1:
The patent segments the motion estimation process into multiple stages with different complexity levels. Different partition types (e.g., 16x16, 8x8, 4x4) are processed using appropriate complexity levels, allowing the system to adapt computational resources to the specific requirements of each partition, thereby improving processing speed while managing implementation difficulty.
Solution Approach 2:
The patent implements dynamic complexity adjustment where the motion estimation algorithm adapts its complexity based on the input characteristics and partition type. The system can dynamically switch between different search algorithms and complexity levels, enabling efficient processing across varying inputs while maintaining manageable hardware implementation requirements.
2Measurement precision
If motion estimation is performed for every partition type and reference picture combination in H.264 encoders, then measurement precision is improved, but computational load increases significantly
Solution Approach 1:
The patent applies different complexity levels and search algorithms to different partition types and reference picture combinations based on their specific requirements. Rather than uniformly applying high complexity to all cases, the system tailors the motion estimation process to each local context, maintaining accuracy where needed while reducing computational load in less demanding scenarios.
Solution Approach 2:
The patent changes key parameters of the motion estimation process dynamically, including search window size, algorithm complexity, and number of reference pictures processed, based on the specific partition type and content characteristics. This allows the system to maintain measurement precision for critical cases while significantly reducing computational load for others.
3Reliability
If conventional motion estimation processes are used, then reliability is maintained, but processing speed decreases due to bottleneck computational load
Solution Approach 1:
The patent introduces dynamic complexity adjustment that allows the system to maintain reliable motion estimation results while adapting processing speed to timing requirements. The system can dynamically select from multiple complexity levels, ensuring reliability is maintained when needed while improving processing speed through optimized lower-complexity paths for suitable cases.
Data Source
AI summary
Scalable motion estimation is provided for macroblocks of a picture with the macroblocks being subdividable into partitions of different shapes and sizes. One embodiment of a method for performing motion estimation includes setting a target complexity for a macroblock of a source frame and performing motion estimation for one or more partitions of the macroblock until the target complexity is met. The macroblock partitions can be rectangular, square or a combination of rectangular and square.