Image Compression System with Content-Sensitive Parameter Adjustment
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Solution Overview
Problem
Current image compression systems, such as those using the H.264 standard, face challenges in real-time hardware implementation due to high computational costs and increased power, bandwidth, and timing requirements, especially with increasing panel resolutions like Full HD or 4K, as they require analyzing multiple block sizes and estimating bits for each combination.
Innovation Solution
An image compression system that dynamically adjusts compression parameters by content-sensitive detection, utilizing historical data to predict parameters without large block analysis, comprising a register for storing pixels, a sensitive detection device for analyzing historical data, a prediction difference device, a bit-rate control device, and an encoding device to generate a bit stream, thereby reducing computation and data stream while maintaining real-time compression/decompression capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple block sizes are tested in parallel to determine segmentation, then compression efficiency is improved, but hardware cost and device complexity increase significantly
Solution Approach 1:
The patent divides the 16x16 block into four 8x8 sub-blocks and further divides each sub-block into four 4x4 sub-blocks, creating a hierarchical segmentation structure. This allows the system to process smaller blocks independently, reducing the computational complexity of testing multiple block sizes while maintaining compression efficiency through content-adaptive selection of block sizes based on texture complexity.
Solution Approach 2:
The patent performs preliminary classification of blocks into texture categories (smooth, medium texture, complex texture) before encoding. By pre-categorizing blocks based on their content characteristics, the system avoids the need to test multiple block sizes in parallel, as the appropriate block size can be determined directly from the texture classification, thereby reducing hardware complexity.
2Productivity
If multiple block sizes are tested in parallel to determine segmentation, then compression efficiency is improved, but processing speed and time delay worsen
Solution Approach 1:
The patent performs preliminary texture complexity classification before encoding decisions. By categorizing blocks into texture types in advance, the system determines the appropriate block size without needing to test multiple sizes in parallel, significantly reducing processing time and enabling real-time compression while maintaining good compression efficiency.
Solution Approach 2:
The patent applies different encoding strategies to different regions of the image based on their local texture characteristics. Smooth regions use larger blocks for better compression, while complex texture regions use smaller blocks to preserve detail. This localized approach improves overall compression efficiency without requiring global analysis of multiple block sizes, thus maintaining processing speed.
3Manufacturing precision
If panel resolution is increased to Full HD or 4K, then image quality is improved, but power consumption and bandwidth requirements increase
Solution Approach 1:
The patent dynamically changes encoding parameters (block size, quantization parameter) based on the content complexity of different image regions. By adapting these parameters locally rather than using fixed high-quality settings across the entire high-resolution image, the system maintains high image quality where needed while reducing power consumption and bandwidth usage in less critical regions.
Solution Approach 2:
The patent applies different compression qualities to different regions of the image based on their importance and texture complexity. Critical regions with important visual information receive higher quality encoding, while less important regions use lower quality settings. This selective approach maintains overall image quality while significantly reducing the total power consumption and bandwidth requirements for high-resolution displays.
4Productivity
If large blocks are analyzed to determine compression parameters, then compression efficiency is improved, but computation cost and time delay increase
Solution Approach 1:
The patent segments the image into smaller 4x4 sub-blocks within each 8x8 block, allowing complexity analysis to be performed on these smaller units rather than on large 16x16 blocks. This segmentation reduces the computational cost of analyzing each block while maintaining compression efficiency by enabling fine-grained adaptation to local content characteristics.
Solution Approach 2:
The patent performs preliminary analysis of texture complexity on small 4x4 sub-blocks to classify the entire 8x8 block into texture categories. By doing this preliminary classification on small units, the system avoids the high computational cost of analyzing large blocks directly, while still achieving good compression efficiency through content-adaptive encoding decisions at the block level.
Data Source
AI summary
An image compression system includes a register, a sensitive detection device, a prediction difference device, a bit-rate control device, a mapping device, and an encoding device, The register temporarily stores plural pixels, which have a current encoding pixel and plural historical decoded pixels. The sensitive detection device analyzes contents of the historical decoded pixels or a previous history data to calculate a prediction value and a current history data. The prediction difference device subtracts the prediction value from the current encoding pixel to generate a prediction difference value. The bit-rate control device adjusts a compression ratio according to a final bits signal so as to output a suggestion bits signal. The mapping device outputs an index based on the prediction difference value, the current history data, and the suggestion bits signal. The encoding device encodes the index to output a bit stream and the final bits signal.