ZTC Codec Digital Image Compression
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Solution Overview
Problem
Existing digital image compression techniques fail to achieve efficient compression ratios while maintaining acceptable image quality, particularly in high-resolution images used in games and virtual environments, leading to data transfer bottlenecks and computational inefficiencies during decompression.
Innovation Solution
The ZTC codec converts digital images into a shift-efficient color space, downsamples chrominance channels, applies discrete cosine transforms with pixel value offsetting, and uses channel-specific quantization tables to achieve tight compression ratios, facilitating fast decompression and reducing data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If digital images are compressed using existing techniques, then data size is reduced, but image quality deteriorates or compression ratio is insufficient
Solution Approach 1:
The image is divided into multiple blocks (e.g., 8x8 pixel blocks), and each block is processed independently through DCT and quantization. This segmentation allows different compression strategies to be applied to different regions, achieving better overall compression while maintaining quality where needed.
Solution Approach 2:
The patent applies quantization tables with different scaling factors to different frequency coefficients in the DCT domain. By adjusting the quantization parameters (scaling factors), the system can control the trade-off between compression ratio and image quality, achieving tighter compression (10-20% better than JPEG) while maintaining acceptable visual quality.
2Manufacturing precision
If high-resolution images are used in games and virtual environments, then image quality is improved, but data transfer time increases and computational efficiency decreases
Solution Approach 1:
The image data is pre-compressed using the ZTC codec algorithm before being stored or transmitted. The compression process includes DCT transformation, quantization, and encoding that prepares the data in an optimized format, reducing the amount of data that needs to be transferred during runtime in gaming or virtual environment applications.
Solution Approach 2:
The patent enables adjustable compression ratios by modifying quantization table scaling factors. This allows the system to optimize between image quality and data size based on specific application requirements, achieving 10-20% better compression than JPEG while maintaining quality suitable for high-resolution displays in games and virtual environments.
3Quantity of substance
If compression ratio is increased, then data size is reduced, but decompression computational cost increases
Solution Approach 1:
The patent uses standardized DCT and quantization tables that can be pre-computed and stored. During decompression, these tables are reused rather than recalculated, significantly reducing the computational burden. The inverse DCT and inverse quantization processes use these pre-prepared tables to efficiently reconstruct the image data.
Solution Approach 2:
The quantization tables use scalable parameters that allow the system to adjust compression intensity. By using appropriately scaled quantization factors, the patent achieves tighter compression (33% better than other texture formats) while keeping the decompression process computationally manageable through efficient inverse transformations.
Data Source
AI summary
A method of compressing a digital image defined by a plurality of pixel values in each of one or more channels includes adjusting each pixel value in each of the one or more channels by an average pixel value for that channel. The method further includes splitting each adjusted channel into one or more image blocks, and converting each image block into a frequency block that is a frequency-domain representation of that image block.