Texture Codec Pre-Processing for Storage and Decompression
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Solution Overview
Problem
Current texture compression methods fail to efficiently reduce storage space requirements for high-definition content on entertainment devices while ensuring fast compression and decompression, which is necessary for smooth accessibility.
Innovation Solution
The proposed solution involves pre-processing image blocks by determining the texture level of each pixel block, assigning a single color or replacing it with an interpolated block based on the texture level, and converting blocks from a 4×4 to an 8×8 configuration for further processing, all while maintaining compatibility with conventional lossless codecs and hardware compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional lossless texture compression is used, then visual quality is preserved, but storage space requirements remain high and decompression speed is insufficient for high-definition content
Solution Approach 1:
The patent applies preliminary action by performing pre-processing on texture data before compression. Specifically, it converts texture data from a first color space to a second color space, and selectively replaces certain pixel values with interpolated values before the compression step. This pre-processing reduces the complexity and variability of the texture data, enabling more efficient compression while maintaining visual quality after decompression.
Solution Approach 2:
The patent utilizes parameter changes by transforming texture data between different color spaces (e.g., from RGB to a different color space representation). This parameter transformation allows the compression algorithm to work more effectively on the transformed data, achieving better compression ratios while preserving the visual information that matters most to human perception.
2Quantity of substance
If texture compression ratio is increased to reduce storage space, then memory requirements decrease, but decompression time increases affecting smooth accessibility
Solution Approach 1:
By performing the color space conversion and selective value replacement as pre-processing steps before compression, the patent prepares the data in a form that is more amenable to efficient compression. This preliminary organization of data reduces the computational complexity during decompression, allowing for faster retrieval and processing of compressed texture data while achieving higher compression ratios.
Solution Approach 2:
The patent segments the texture processing into distinct stages: color space conversion, selective value replacement based on texture complexity analysis, and compression. This segmentation allows each stage to be optimized independently, with the pre-processing stages preparing data to enable faster decompression of the compressed representation.
3Quantity of substance
If advanced compression algorithms are used to improve compression ratio, then storage efficiency increases, but device complexity and processing requirements increase
Solution Approach 1:
The pre-processing steps of color space conversion and selective value replacement are designed to be computationally efficient and can be performed during asset creation or loading. This shifts the computational burden to pre-processing time rather than real-time decompression, reducing the complexity requirements for the decompression hardware while maintaining high compression efficiency.
Solution Approach 2:
By changing the color space parameters and selectively processing only certain pixel values based on texture complexity, the patent reduces the amount of data that requires complex processing. This parameter-based selective processing simplifies the overall processing requirements while achieving better storage efficiency.
Data Source
AI summary
Techniques for pre-processing image blocks containing texture are disclosed herein which reduce memory requirements without degradation of quality. This is accomplished by determining the level of texture in each pixel block of the image and then performing a compression algorithm on such pixel block. Various methods are available to determine the texture level, including identifying the luminescence variation or difference within the pixel block. Depending on the amount of texture level contained within each pixel block, pre-processing may involve assigning the pixel block a single color or replacing the pixel block with an interpolated pixel block. Pre-processing on the pixel block may not be performed when the texture level therein is determined to be at a predetermined amount or level. Additional techniques are also disclosed herein which involve full processing of an image block.