Image Encoding Method with Adaptive Quantization for High-Resolution Displays
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Solution Overview
Problem
Existing image encoding methods face challenges in maintaining image quality due to limited bandwidth in display links, particularly for high-resolution or high-definition images, as they often result in decreased image quality or efficiency, especially for pixels with large residuals or positioned at boundaries within pixel groups.
Innovation Solution
An image encoding method that derives luminance and chrominance prediction values, adjusts quantization parameters based on residual properties, and applies a modification factor to optimize bit allocation and minimize image distortion, while also using candidate coefficients and quantization tables to enhance encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution or high-definition image data is transmitted through a display link with limited bandwidth, then image quality can be maintained, but transmission efficiency decreases and bandwidth requirements increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization parameters based on residual properties and pixel positions. Different quantization strengths are applied to different regions (e.g., stronger quantization for pixels with small residuals, weaker quantization for pixels with large residuals or at boundaries), optimizing the balance between compression ratio and image quality reconstruction
Solution Approach 2:
The patent implements local quality by applying different encoding strategies to different pixel groups based on their specific characteristics. Pixels at boundaries of pixel groups receive special treatment with adjusted quantization parameters to prevent boundary artifacts, while pixels with small residuals use stronger compression. This localized adaptation maintains overall image quality while improving transmission efficiency
2Productivity
If existing image encoding methods are used to compress high-resolution image data, then transmission bandwidth is reduced, but image quality decreases especially for pixels with large residuals or at pixel group boundaries
Solution Approach 1:
The patent dynamically changes quantization parameters based on residual properties calculated from previous and current image data. The system adjusts quantization strength according to the magnitude of residuals and pixel positions, applying weaker quantization where needed to preserve image quality while maintaining overall compression efficiency
Solution Approach 2:
The patent applies local quality enhancement by specifically protecting pixels at pixel group boundaries and pixels with large residuals from excessive quantization. These critical pixels receive adjusted quantization parameters to prevent artifacts and maintain local image quality, while other pixels can be compressed more aggressively
3Quantity of substance
If quantization is applied to compress image data, then bandwidth usage is reduced, but distortion increases particularly at pixel boundaries
Solution Approach 1:
The patent implements local quality protection by detecting pixels at boundaries of pixel groups and applying adjusted quantization parameters to these specific locations. This prevents the excessive distortion and artifacts that typically occur at boundaries during quantization, while still achieving overall data reduction
Solution Approach 2:
The patent changes quantization parameters dynamically based on pixel position and residual properties. By adjusting the quantization step size for boundary pixels and pixels with large residuals, the system reduces distortion in critical areas while maintaining compression ratios through stronger quantization in less sensitive areas
Data Source
AI summary
All image-displaying method includes deriving a luminance prediction value, calculating a luminance residual, deriving a chrominance prediction value, calculating a first chrominance residual, deriving a quantized luminance value by quantizing the luminance residual, deriving an inverse quantized luminance value by inverse quantizing the quantized luminance value, selecting one of candidate coefficients as a modification factor, calculating a second chrominance residual by subtracting an adjustment value from the first chrominance residual (wherein the adjustment value is equal to the inverse quantized luminance value multiplied by the modification factor), deriving a quantized chrominance value by quantizing the second chrominance residual, encoding the quantized luminance value and the quantized chrominance value to produce encoded data, decoding the encoded data to obtain decoded image data, and controlling a display device according to the decoded image data to display an image.