Adaptive Transform Type Selection for Lower-Complexity Video Encoding
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Solution Overview
Problem
Existing video coding techniques face inefficiencies in identifying the optimal transform type for encoding blocks, leading to suboptimal bandwidth utilization and increased processing complexity.
Innovation Solution
Adaptive transform type sets are determined based on frame level statistics, using transform type statistics from previously reconstructed reference frames to identify a subset of optimal transform types for encoding current blocks, thereby optimizing bandwidth utilization and reducing processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full set of transform types is used for encoding current blocks, then the probability of selecting the optimal transform type increases, but the bandwidth utilization increases and processing complexity increases
Solution Approach 1:
The full set of transform types is segmented into multiple subsets based on frame level statistics. Different subsets are selectively applied to different current blocks depending on the characteristics of previously reconstructed reference frames. This segmentation allows the system to use a limited subset for most blocks (saving bandwidth) while still providing access to the full range of transform types when needed (maintaining reliability).
Solution Approach 2:
The transform type set is made dynamic by adapting it based on frame level statistics from previously reconstructed reference frames. The encoder dynamically determines which transform types to include in the current subset based on the actual content characteristics observed in reference frames, rather than using a static fixed set. This dynamic adaptation optimizes the balance between selection probability and bandwidth usage.
2Reliability
If a full set of transform types is used for encoding current blocks, then the probability of selecting the optimal transform type increases, but the processing complexity increases
Solution Approach 1:
The processing complexity is reduced by segmenting the transform type selection process. Instead of evaluating all transform types for every current block, the system pre-segments transform types into subsets based on frame level statistics. This segmentation allows the encoder to process only the relevant subset for each block, significantly reducing the computational burden while maintaining the ability to select the optimal transform type when it exists in the subset.
Solution Approach 2:
The system performs preliminary action by pre-determining transform type subsets based on frame level statistics from previously reconstructed reference frames before encoding the current blocks. This preliminary segmentation of transform types based on observed statistics allows the actual encoding process to work with pre-filtered, context-appropriate subsets rather than the full set, reducing real-time processing complexity.
3Loss of energy
If transform type statistics from previously reconstructed reference frames are used to determine a subset of transform types, then bandwidth utilization is minimized and processing complexity is reduced, but the probability of selecting the optimal transform type may decrease
Solution Approach 1:
The system uses feedback from previously reconstructed reference frames to inform transform type subset selection for current blocks. By analyzing transform type statistics from reference frames and using this feedback to determine appropriate subsets for current blocks, the system creates a closed-loop adaptation mechanism. This feedback-driven approach ensures that the subset selection is based on actual content characteristics, maximizing the probability of including the optimal transform type while maintaining subset limitations for bandwidth efficiency.
Data Source
AI summary
Encoding using adaptive transform type sets based on frame level statistics includes obtaining an encoded bitstream by encoding a current block of a current frame of a current sequence of frames of an input video stream using adaptive transform type sets based on frame level statistics and outputting the encoded bitstream. Encoding the current block includes obtaining transform type statistics for previously reconstructed reference frames from the current sequence of frames, the previously reconstructed reference frames including at least one previously reconstructed reference frame, determining, in accordance with the transform type statistics, a current subset of transform types from a set of available transform types, generating encoded block data for the current block using a current transform type from the current subset of transform types, and including the encoded block data in the encoded bitstream.