Image Decoding Arithmetic Right Shift Prediction Correction
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Solution Overview
Problem
Current image encoding and decoding technologies face challenges in reducing computation complexity, which affects the efficiency of image encoding and decoding processes, especially with the increasing demand for high-resolution images.
Innovation Solution
The method involves reconstructing residual blocks through inverse-quantization and inverse-transformation, generating prediction blocks through intra-prediction, and correcting prediction values by performing arithmetic right shifts on two's complementary integer representations to reduce computation complexity and enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional intra prediction and inter prediction methods are used for high-resolution images, then image quality is maintained, but computation complexity increases significantly
Solution Approach 1:
The prediction process is segmented into multiple stages: generating initial prediction values from reference pixels, calculating correction values based on gradient computations, and applying final corrections. This segmentation allows each stage to be optimized independently, reducing overall computation complexity while maintaining image quality.
Solution Approach 2:
Reference pixels are pre-processed and stored before the actual prediction operation. Gradient values and correction terms are calculated in advance based on reference pixel differences, so that during prediction, only simple additions and lookups are needed, significantly reducing real-time computation complexity.
2Productivity
If complex prediction algorithms are applied to reduce computation complexity, then encoding efficiency improves, but prediction accuracy deteriorates
Solution Approach 1:
The patent introduces correction values that are functions of position coordinates (x, y) within the current block. By parameterizing the correction term based on pixel position and reference pixel differences, the algorithm achieves adaptive prediction accuracy without requiring complex per-pixel computations, thus improving encoding efficiency while maintaining precision.
Solution Approach 2:
Correction values serve as intermediaries between the simple reference pixel copying and the final prediction result. These correction terms, derived from gradient computations on reference pixels, mediate the prediction process by adjusting initial prediction values, achieving high accuracy with computationally efficient operations.
3Measurement precision
If more reference pixels are used for prediction, then prediction accuracy improves, but memory access and computation time increase
Solution Approach 1:
The patent extracts only the essential reference pixels needed for computing correction values (typically corner or boundary reference pixels) rather than using all available reference pixels. This extraction approach maintains prediction accuracy by capturing the dominant gradient information while minimizing memory access and computation time.
Data Source
AI summary
An image decoding method according to the present invention includes reconstructing a residual block by inverse-quantizing and inverse-transforming an entropy-decoded residual block, generating a prediction block by performing intra prediction on a current block, and reconstructing an picture by adding the reconstructed residual block to the prediction block, wherein generating the prediction block includes generating a final prediction value of a prediction target pixel included in the current block based on a first prediction value of the prediction target pixel and a final correction value calculated by performing an arithmetic right shift on a two's complementary integer representation for an initial correction value of the prediction target pixel by a binary digit of 1. Accordingly, upon image encoding/decoding, computation complexity may be reduced.