Image Decoding with Quantization Index Shifting and Weighted Reconstruction
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images leads to a significant increase in data volume, resulting in higher transmission and storage costs, and existing image encoding/decoding technologies struggle to efficiently minimize the difference between original and reconstructed residual signals.
Innovation Solution
The method involves deriving a first and second quantization index, shifting the first index by a predetermined integer value, and applying weights to reconstructed transformed coefficients to generate final coefficients, with control mechanisms based on quantization parameters and block positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantization is applied to compress image data, then data volume is reduced, but the difference between original and reconstructed residual signals increases
Solution Approach 1:
The patent applies quantization index shifting by modifying the quantization parameter (QP) dynamically. A shift value is added to the base QP to create an adjusted quantization parameter, which changes the quantization step size adaptively. This parameter change allows the system to optimize between compression ratio and reconstruction accuracy by adjusting the quantization intensity based on local image characteristics and rate-distortion constraints.
2Manufacturing precision
If high-resolution, high-quality image encoding is used, then image quality is improved, but transmission and storage costs increase
Solution Approach 1:
The patent dynamically adjusts the quantization parameter through shifting operations to optimize the balance between image quality and compression efficiency. By adaptively changing QP values based on rate-distortion optimization, the system achieves high image quality where needed while maintaining lower bitrates in appropriate regions, thereby reducing overall transmission and storage costs.
Solution Approach 2:
The quantization index shifting is applied selectively based on local image characteristics, block positions, and rate-distortion constraints. Different regions of the image can have different effective quantization parameters, allowing high-quality encoding in important regions while using more aggressive compression in less critical areas, thus optimizing the overall quality-cost ratio.
3Productivity
If quantization index shifting is applied, then coding efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent implements quantization index shifting by adding a shift value to the base quantization parameter. This operation is computationally simple compared to full rate-distortion optimization, providing an efficient approximation that improves coding performance without requiring complex iterative calculations, thus achieving better productivity with acceptable complexity.
Data Source
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AI summary
According to an embodiment of the present invention, a method for decoding an image is provided including deriving a first quantization index, deriving a second quantization index by shifting the first quantization index, deriving a first reconstructed transformed coefficient and a second reconstructed transformed coefficient by performing dequantization on the first quantization index and the second quantization index respectively, and generating final reconstructed transformed coefficients by applying a first weight and a second weight to the first reconstructed transformed coefficient and the second reconstructed transformed coefficient respectively.