Hierarchical Motion Estimation for Video Encoding Complexity
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Solution Overview
Problem
The high computational complexity of motion estimation in video encoding leads to significant time consumption, making it a bottleneck in video compression processes, despite its importance for achieving efficient and precise encoding.
Innovation Solution
A system and method that employs a fast motion estimation algorithm using a multi-level hierarchical video structure, where down-sampled images at different resolution levels are used to facilitate efficient motion estimation by leveraging dissimilarity levels and applying specific rules for mode selection and motion search, reducing the complexity and increasing the speed of the encoding process without compromising quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional motion estimation is used to ensure precise encoding, then encoding quality is improved, but encoding time increases significantly
Solution Approach 1:
The video frame is divided into multiple resolution levels (e.g., 1/4 resolution, 1/2 resolution, full resolution). Motion estimation is performed hierarchically starting from the lowest resolution level, where coarse motion vectors are obtained first, then refined at progressively higher resolution levels. This segmentation of the estimation process across multiple scales reduces the total search space and computational complexity while maintaining encoding quality.
Solution Approach 2:
Motion estimation is performed in advance at lower resolution levels before processing the full-resolution frame. The coarse motion vectors obtained from lower resolution levels serve as preliminary estimates that guide the subsequent refinement process at higher resolutions, reducing the search space and computational requirements for the final precise motion estimation.
2Manufacturing precision
If full-resolution motion estimation is performed to maintain quality, then encoding precision is improved, but computational complexity increases
Solution Approach 1:
The motion estimation process is segmented into multiple resolution levels. At each level, the frame is processed at a reduced resolution (e.g., 1/4, 1/2 of original), and motion vectors are estimated hierarchically. This segmentation reduces the number of pixels that need to be processed at full resolution, thereby reducing computational complexity while maintaining estimation precision through progressive refinement.
Solution Approach 2:
The problem is solved by adding a resolution dimension to the motion estimation process. Instead of performing a single full-resolution estimation, the method operates across multiple resolution dimensions, starting from coarse (low resolution) and progressing to fine (high resolution). This dimensional approach transforms a computationally intensive 2D search into a multi-stage process that reduces overall complexity.
Data Source
AI summary
Techniques for coding of video content are presented. An encoder component generates a down-sampled image(s) of a video frame of a video frame sequence to form a multi-resolution level video structure. Using a fast motion estimation algorithm, the encoder component performs motion estimation on a lowest-resolution image, propagates estimation results to the next-higher resolution level. The encoder component uses those estimation results, and/or spatial information of previous encoded macroblocks of the next-higher resolution image, as a prediction for motion estimation performed on the next-higher resolution image. The encoder component applies mode selection and motion search rules, and calculates and uses dissimilarity values of macroblocks, to facilitate prediction for motion estimation at the next-higher resolution level. The encoder component can continue this motion estimation process for another higher-resolution image(s) of the structure up through the original image.