Rate-Distortion Optimization With Decoder Complexity Scaling
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Solution Overview
Problem
Existing video encoding methods do not effectively balance rate distortion and computational complexity, leading to inefficient compression and decoding processes.
Innovation Solution
A method and apparatus that incorporate a complexity factor to scale cost functions during rate distortion optimization, considering factors such as MACs per pixel, memory accesses, and instructions, to determine the optimal coding option for video encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex coding options are used to improve compression efficiency, then rate distortion performance is improved, but decoder computational complexity increases
Solution Approach 1:
The patent introduces a complexity factor that scales the cost function based on decoder complexity metrics (MACs per pixel, memory accesses, instructions). By dynamically adjusting the cost function parameters to account for decoder complexity, the system selects coding options that optimize the balance between compression efficiency and decoder computational load, rather than simply minimizing bitrate or distortion alone
Solution Approach 2:
The system dynamically adjusts the cost function by applying a complexity factor that varies depending on the specific coding option being evaluated. This allows the rate distortion optimization to adaptively weigh different coding options based on their relative decoder complexity, enabling flexible trade-off management between compression performance and computational requirements
2Manufacturing precision
If complex coding options are used to improve rate distortion optimization, then encoding quality is improved, but encoding computational complexity increases
Solution Approach 1:
The patent modifies the cost function parameters by introducing a complexity factor that reflects encoder computational requirements. This allows the rate distortion optimization to consider both quality metrics and computational costs when selecting coding options, preventing the selection of overly complex coding paths that would degrade encoder performance
Solution Approach 2:
The system incorporates feedback about computational complexity into the cost function evaluation process. By measuring or estimating the computational complexity of each coding option and feeding this information back into the optimization decision, the system can avoid selecting coding options that would excessively burden the encoder, thereby maintaining efficient encoding operations
Data Source
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AI summary
In one implementation, an encoder obtains a first value indicating a number of bits used to encode a current block in a picture, under a coding option, and obtains a second value indicating a distortion between a reconstructed version and an original version of the current block associated with the coding option. The encoder obtains a cost function based on the first value and the second value for the current block. The encoder also obtains another cost function for the current block, associated with a current best coding option. By comparing the cost function and the another cost function, wherein the cost function or the another cost function is scaled by a complexity factor indicating computational complexity associated with the coding option, the encoder updates the another cost function and the current best coding option based on the comparison result.