Integerized Interpolation Filters for Efficient Video Prediction
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Solution Overview
Problem
Existing video coding technologies face inefficiencies due to the use of floating-point filter coefficients in interpolation filters, leading to instability and increased computational costs, which limits interoperability and coding efficiency.
Innovation Solution
Implement integerization methods that generate a set of filter candidates by considering two possible integerized values for each filter coefficient, evaluate them using an error metric, and select the filter with the lowest error to maintain desirable properties and improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If floating-point filter coefficients are used in interpolation filters, then filtering precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent changes the parameter type of filter coefficients from floating-point to integer. This parameter change maintains sufficient filtering precision while dramatically reducing computational complexity. Integer arithmetic operations are simpler and faster to execute than floating-point operations, directly resolving the contradiction between precision and computational complexity.
2Measurement precision
If floating-point filter coefficients are used in interpolation filters, then filtering precision is improved, but processing speed decreases
Solution Approach 1:
The patent changes the parameter type of filter coefficients from floating-point to integer. This parameter change maintains sufficient filtering precision while dramatically improving processing speed. Integer arithmetic operations are simpler and faster to execute than floating-point operations, directly resolving the contradiction between precision and processing speed.
3Measurement precision
If floating-point filter coefficients are used in interpolation filters, then filtering accuracy is improved, but interoperability and stability worsen
Solution Approach 1:
The patent changes the parameter type of filter coefficients from floating-point to integer. Integer representation is deterministic and architecture-independent, ensuring consistent behavior across different platforms and devices. This resolves the interoperability and stability issues while maintaining sufficient filtering accuracy through careful selection of integer quantization levels.
Data Source
AI summary
In some embodiments, a video encoder encodes a video into a video bitstream. The video encoder accesses a set of frames of the video and performs inter prediction for the set of frames using a set of integerized interpolation filters to generate prediction residuals to be encoded into the video bitstream. The set of integerized interpolation filters are generated by integerizing a set of interpolation filters, each of the set of interpolation filters having floating-point filter coefficients. For each interpolation filter, two integerized filter coefficient values are generated for each filter coefficient and a set of filter candidates are generated based on the two integerized values for each filter coefficient. An error metric for each filter candidate is calculated and an integerized interpolation filter having the lowest error metric is selected for the interpolation filter from the set of filter candidates.