Video Encoder Block Merging for Motion Estimation
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Solution Overview
Problem
Current video encoding methods, such as the H.264 standard, face challenges in achieving efficient and fast encoding and decoding, particularly in motion estimation, which affects bit savings and encoding quality, especially at high quantization levels and in regions with little detail.
Innovation Solution
The implementation of a dynamic search region and block merging methodology within the motion search module, combined with refined motion estimation techniques, allows for more accurate motion vector determination and efficient encoding, leveraging modules like motion search, refinement, and intra-prediction to optimize video processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion estimation methods are used in video encoding, then encoding can be performed with standard computational resources, but encoding quality and bit savings are insufficient, especially at high quantization levels and in regions with little detail
Solution Approach 1:
The video frame is divided into multiple blocks, and motion estimation is performed on selected blocks based on cost matrix evaluation. This segmentation allows the system to focus computational resources on blocks that benefit most from accurate motion estimation, improving overall encoding quality while maintaining reasonable encoding speed.
Solution Approach 2:
The patent applies different processing strategies to different blocks based on their characteristics. Blocks with little detail or at high quantization levels receive refined motion estimation with dynamic search regions, while other blocks use standard methods. This local quality approach optimizes the balance between encoding accuracy and computational burden.
2Measurement precision
If refined motion estimation with dynamic search region and block merging is implemented, then encoding quality and bit savings improve, but computational burden increases
Solution Approach 1:
The search region is made dynamic rather than fixed, allowing the system to adapt the search area based on the specific block characteristics and motion patterns. This dynamic adjustment reduces unnecessary computations in regions where extensive search is not needed, while maintaining accuracy where required.
Solution Approach 2:
Adjacent blocks with similar motion characteristics are merged and processed together using a combined cost matrix. This merging reduces the total number of motion estimation operations required, decreasing computational burden while maintaining encoding quality for regions with uniform motion.
3Productivity
If block merging based on cost matrix evaluation is used, then encoding efficiency improves through reduced computational burden, but implementation complexity increases
Solution Approach 1:
Cost matrices are evaluated and blocks are identified for merging before the actual motion estimation process. This preliminary action allows the system to pre-determine which blocks should be processed together, simplifying the subsequent encoding process and making the implementation more manageable despite the added functionality.
Data Source
AI summary
A video encoder includes a motion search module that determines a motion search motion vector for a region of a selected picture of the plurality of pictures. The motion search module determines the region by merging selected ones of a plurality of blocks of the selected picture based on an evaluation of a cost matrix associated with the plurality of blocks of the selected picture.