Encoder Decision Tree for RD-Cost Complexity Trade-off
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Solution Overview
Problem
Existing video compression systems face challenges in efficiently trading off rate-distortion cost (RD-cost) and computational complexity, particularly in real-time applications where minimizing delay is crucial, and previous learning-based approaches ignore individual error costs.
Innovation Solution
The proposed solution involves configuring an encoder using reinforcement learning, where the encoder segmentally traverses a conditional subset of operations and makes decisions based on a decision function that considers features obtained from preceding operations, optimizing a predetermined criterion while managing computational effort. This is achieved through a processor-operated decision tree that learns from training examples, weighing time consumption and coding efficiency to determine optimal parameters for the decision function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a search algorithm is used to find optimized syntax representation, then RD-cost is improved, but computational complexity increases
Solution Approach 1:
The encoder operations are organized into a segmented decision tree structure where the search space is divided into multiple levels. Each level processes a subset of operations, allowing the encoder to explore potential syntax representations in a structured manner without exhaustively searching all possibilities, thus balancing RD-cost optimization with computational complexity management.
Solution Approach 2:
The decision tree parameters are dynamically adjusted based on encoding context and performance feedback. The traversal depth, operation selection, and decision criteria are adapted in real-time to optimize the trade-off between RD-cost and computational complexity for different video content characteristics and encoding conditions.
2Manufacturing precision
If more encoder operations are executed to optimize RD-cost, then encoding quality improves, but encoding time increases
Solution Approach 1:
The decision tree is pre-configured with operation sequences and decision criteria based on training data and expected video characteristics. This preliminary preparation allows the encoder to make informed decisions about which operations to execute and in what order, avoiding unnecessary computations and reducing encoding time while maintaining RD-performance.
Solution Approach 2:
The decision tree enables selective skipping of operations that are unlikely to improve RD-cost based on preliminary assessments of video content and current encoding state. The encoder can rush through less promising operation sequences and focus computational resources on more promising paths, thereby reducing overall encoding time.
3Device complexity
If learning-based approaches are used to reduce complexity, then computational effort decreases, but individual error costs are ignored
Solution Approach 1:
The decision tree incorporates feedback mechanisms where the outcomes of encoder operations are evaluated and used to adjust subsequent decisions. The system learns from actual encoding results and error costs, refining its operation selection and sequencing to minimize both computational effort and error costs, rather than ignoring individual error costs as in previous approaches.
Data Source
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AI summary
A more effective configuration of an encoder for encoding a media signal in units of segments by encoding each segment by executing at least one of a set of encoder operations may be rendered more effectively is achieved by having the encoder segmentally traversing at least a conditional subset of the set of encoder operations and making, for each operation of the conditional subset, a decision on executing the respective encoder operation on the basis of a decision function. If the decision function indicates that the respective encoder operation is to be executed, the encoder executes the respective encoder operation so as to obtain a set of features and the arguments of the decision function on the basis of which the decision on executing the respective encoder operation is made comprises the sets of features obtained by the execution of preceding encoder operations. Then, the encoder determines which of an executed subset of the set of encoder operations results in a coded representation of the respective segment which is extremal in terms of a predetermined optimization criterion. The coded representation of the encoder operation thus determined is used for encoding the respective segment. The encoder thus prepared enables to be configured in a manner which trades off maximizing the predetermined maximization criterion on the one hand and some other criterion on the other hand, namely computational effort. Configuration of such encoder(s) is also described.