Bit-Depth Remapping for Guaranteed Image Data Compression
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Solution Overview
Problem
Current data compression methods for GPUs and other processing units face challenges in reducing memory bandwidth and storage space efficiently, particularly when dealing with image data, as they often require significant power consumption and do not guarantee a compression threshold, leading to inefficiencies in memory transfer and access.
Innovation Solution
A method is introduced that determines the bit depth of input data by comparing its total size to pre-defined threshold values, using a mapping parameter to reduce bit depth and encode it in the compressed data, ensuring a guaranteed compression ratio through a combination of lossless and lossy compression techniques, and incorporating a bit depth encoding element to maintain data integrity during decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data compression is applied to reduce memory bandwidth and storage space, then power consumption is reduced, but the compression ratio may not be guaranteed to meet a threshold
Solution Approach 1:
The patent applies lossy compression techniques (such as reducing bit depth from 10-bit to 8-bit) as a preliminary action before applying lossless compression. This pre-processing step reduces the data size and complexity, enabling the subsequent lossless compression to more reliably achieve the target compression ratio threshold while maintaining acceptable data quality.
Solution Approach 2:
The patent changes the bit depth parameter of the image data as a key mechanism. By converting image data from higher bit depth (e.g., 10-bit) to lower bit depth (e.g., 8-bit), the patent achieves significant size reduction. This parameter change is applied selectively based on whether the compression threshold can be met, thereby controlling the trade-off between compression ratio guarantee and data quality.
2Measurement precision
If higher quality rendering algorithms are used, then image quality is improved, but memory bandwidth consumption increases
Solution Approach 1:
The patent changes the bit depth parameter of image data from higher precision (10-bit) to lower precision (8-bit) formats, thereby reducing the quantity of data that needs to be transferred over memory bandwidth while maintaining acceptable image quality for most applications.
Solution Approach 2:
The patent applies different compression strategies to different data channels (e.g., Y, U, V components in YUV format) based on their relative importance to perceived image quality. The luminance channel (Y) retains higher precision while chrominance channels (U, V) are compressed more aggressively, optimizing the balance between image quality and memory bandwidth usage.
3Quantity of substance
If bit depth is reduced to achieve compression, then data size is reduced, but data precision and quality are degraded
Solution Approach 1:
The patent segments image data into different components (e.g., YUV color space separation into luminance Y and chrominance U, V) and applies different bit depth reduction strategies to each segment. The luminance component, which has greater impact on perceived quality, retains more precision, while chrominance components are compressed more aggressively.
Solution Approach 2:
The patent selectively applies bit depth reduction (e.g., from 10-bit to 8-bit) based on whether the compression threshold can be achieved. This conditional parameter change ensures that data precision is only reduced when necessary to meet compression requirements, and the full precision is retained when the threshold is already satisfied.
Data Source
AI summary
A method of data compression in which the total size of the compressed data is determined and based on that determination, the bit depth of the input data may be reduced before the data is compressed. The bit depth that is used may be determined by comparing the calculated total size to one or more pre-defined threshold values to generate a mapping parameter. The mapping parameter is then input to a remapping element that is arranged to perform the conversion of the input data and then output the converted data to a data compression element. The value of the mapping parameter may be encoded into the compressed data so that it can be extracted and used when subsequently decompressing the data.