Motion Estimation for Video Frame Rate Conversion
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Solution Overview
Problem
Existing motion estimation algorithms for video frame rate conversion face performance limitations, power constraints, and artifacts such as non-smooth operations and motion artifacts, particularly in multi-core systems and mobile devices, due to inefficient scanning patterns and insufficient synchronization between threads.
Innovation Solution
A motion estimation method that determines whether a current frame unit is a double block, calculates confidence function values for candidate vectors, and selects estimated motion vectors based on these values, using spatial, temporal, and random candidate vectors, while allowing for parallel processing in multiple threads and diagonal scanning to improve performance and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If motion estimation is performed using multiple parallel threads to improve processing speed, then productivity increases, but memory access non-locality increases causing performance degradation
Solution Approach 1:
The frame is divided into multiple independent block groups that can be processed by different threads simultaneously. Each thread processes a specific block group with local memory access patterns, eliminating the non-locality problem while maintaining parallel processing benefits.
Solution Approach 2:
The patent introduces a hierarchical processing structure where blocks are organized in groups with specific spatial relationships. This dimensional organization allows threads to access memory in a more localized manner while still enabling parallel processing across different block groups.
2Productivity
If block scanning directions are made opposite in adjacent parts to enable parallel processing, then productivity increases, but interlaced effects occur on object edges
Solution Approach 1:
Different scanning directions are applied to different block groups based on their spatial location and characteristics. This local adaptation allows parallel processing while maintaining appropriate scanning directions for each region to avoid interlaced effects on object edges.
Solution Approach 2:
The scanning direction is made dynamic and adaptive rather than fixed. Threads can adjust their scanning directions based on local image characteristics and motion patterns, allowing parallel processing while maintaining accuracy by avoiding rigid opposite directions that cause interlacing.
3Ease of operation
If motion estimation uses blocks spaced apart to enable thread independence, then ease of operation increases, but estimation accuracy decreases
Solution Approach 1:
The frame is segmented into block groups with specific spatial relationships. Threads process these groups independently for ease of operation, while the segmentation strategy ensures that each group contains or has access to necessary adjacent blocks for accurate motion estimation.
Solution Approach 2:
The patent introduces intermediary data structures and memory buffers that allow threads to access necessary adjacent blocks without direct inter-thread dependency. This intermediary mechanism maintains thread independence while preserving access to locally adjacent blocks for accurate estimation.
4Manufacturing precision
If frame rate is increased to improve motion smoothness, then video quality improves, but power consumption increases
Solution Approach 1:
Instead of performing full motion estimation for all blocks at high frame rates, the patent applies motion estimation selectively to important regions and uses simplified methods for other areas. This partial action approach maintains motion smoothness where needed while reducing overall computational load and power consumption.
Solution Approach 2:
The patent dynamically changes motion estimation parameters such as search range, block size, and algorithm complexity based on local image characteristics and motion activity. This allows high-quality estimation in regions requiring smooth motion while using lower-complexity methods elsewhere, optimizing the balance between motion smoothness and power consumption.
Data Source
AI summary
A motion estimation method for video data including frames and an apparatus therefor are provided. The motion estimation method includes determining whether a current frame unit for which motion estimation is to be performed corresponds to a double block, when the current frame unit corresponds to a double block, acquiring a candidate vector set corresponding to a first single block included in the double block, as a candidate vector set of the double block, individually calculating a confidence function value of each candidate vector included in the candidate vector set of the double block, for the first single block and a second single block included in the double block, and acquiring an estimated motion vector of the first single block and an estimated motion vector of the second single block, based on the calculated confidence function value of each candidate vector.