Affine Intra Prediction with Reduced Neighboring Samples
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Solution Overview
Problem
Existing video coding technologies face challenges in achieving efficient compression of video data, particularly in intra-prediction modes, due to high computational complexity and resource consumption associated with multiplications in affine transformations.
Innovation Solution
The use of affine linear weighted intra-prediction (ALWIP) transformations, which involve reducing neighboring sample sets through averaging or downsampling, followed by linear or affine transformations, to derive predicted values, and optionally using interpolation for further samples, thereby reducing the number of multiplications required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If affine transformations are used for intra-prediction, then prediction accuracy is improved, but computational complexity increases due to high number of multiplications
Solution Approach 1:
The patent changes the parameters of the transformation by using a reduced set of neighboring samples instead of all neighboring samples. This reduces the number of multiplication operations required while maintaining prediction accuracy through the linear or affine transformation of the reduced sample set.
Solution Approach 2:
The patent applies partial action by using only a subset of neighboring samples (reduced set) rather than all available neighboring samples. This partial sampling approach reduces computational complexity while the transformation process compensates to maintain sufficient prediction accuracy.
2Measurement precision
If all neighboring samples are used for intra-prediction, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent uses only a reduced set of neighboring samples instead of processing all neighboring samples. This partial action reduces the number of operations required, thereby decreasing processing time while maintaining acceptable prediction accuracy through the transformation process.
Solution Approach 2:
The patent segments the neighboring samples into a reduced set that is sufficient for accurate prediction. By dividing and selecting only the necessary samples, the processing time is reduced while prediction accuracy is maintained through the linear or affine transformation of these segmented samples.
3Use of energy by moving object
If the number of multiplication operations is reduced, then computational power consumption is reduced, but prediction accuracy may deteriorate
Solution Approach 1:
The patent changes the approach by transforming a reduced set of samples using linear or affine transformations. This parameter change in the transformation method allows reducing multiplication operations while maintaining prediction accuracy through the mathematical properties of the transformation.
Solution Approach 2:
The patent substitutes the mechanical system of using all neighboring samples directly with a mathematical transformation approach. By replacing direct sample usage with linear or affine transformations of reduced samples, the system reduces computational power consumption while maintaining prediction accuracy through mathematical modeling.
Data Source
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AI summary
There are provided techniques for encoding/decoding of video signals, e.g. implemented in decoders, encoder, methods and non-transitory storage units storing instructions to perform the methods. A decoder or encoder may be configured to predict a predetermined block of the picture using a plurality of neighbouring samples by reducing the plurality of neighbouring samples to obtain a reduced set of samples values lower, in number of samples, than compared to the plurality of neighbouring samples, subjecting the reduced set of sample values to a linear or affine linear transformation to obtain predicted values for predetermined samples of the predetermined block.