Compressing High Dynamic Range Image Data into Low Dynamic Range Format
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Solution Overview
Problem
High dynamic range (HDR) image data requires significant memory and bandwidth, limiting system performance and increasing costs due to its storage in floating point format, whereas low dynamic range (LDR) data is more memory-efficient but lacks the range of HDR data.
Innovation Solution
HDR image data is synthesized and compressed into an LDR format using multi-sample anti-aliasing, with compressed sub-pixel samples combined to create filtered data, allowing for storage in an LDR render target and subsequent reconstruction for post-processing functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HDR image data is stored in floating point format buffer, then image quality and dynamic range are maintained, but memory requirements and bandwidth increase significantly
Solution Approach 1:
The patent changes the data format parameter from floating point (HDR) to fixed point (LDR) representation. By scaling and compressing the dynamic range of the image data, the system can store HDR image information in LDR format, reducing memory requirements while maintaining acceptable image quality for most display applications.
Solution Approach 2:
The patent creates a compressed copy of the HDR image data in LDR format for storage in render targets. This compressed representation is sufficient for many post-processing operations and display purposes, avoiding the need to maintain full precision HDR data throughout the entire rendering pipeline.
2Adaptability or versatility
If HDR image data is stored in floating point format, then dynamic range is preserved, but system cost and memory bandwidth increase
Solution Approach 1:
The system changes the storage parameter from high-precision floating point to lower-precision fixed point format. This parameter change reduces memory bandwidth requirements and system cost while maintaining adequate dynamic range for the intended application, as many post-processing operations can work effectively with compressed LDR data.
3Measurement precision
If multi-sample anti-aliasing is used to synthesize HDR image data, then visual quality improves, but processing time and memory usage increase
Solution Approach 1:
The patent performs preliminary compression of the HDR image data into LDR format during the rendering process itself, before the data needs to be stored or used for post-processing. This preliminary action reduces the amount of data that needs to be processed in subsequent stages, effectively reducing overall processing time despite the additional compression steps.
Solution Approach 2:
The patent extracts only the essential information from the multi-sample HDR data by compressing it into a reduced-precision LDR format. This extraction retains the most important visual information while discarding redundant precision that would increase processing time and memory usage without providing noticeable visual improvement.
Data Source
AI summary
Systems and methods for storing high dynamic range image data in a low dynamic range format may be used to store the high dynamic range image data in less memory. The memory bandwidth needed to access the high dynamic range data is reduced and processing performance may be improved when performance is limited by memory bandwidth. The high dynamic range image data is scaled and compressed into a low dynamic range format for storage in a render target. If the compressed high dynamic range image data contains multiple data samples per pixel, the data may be processed to produce filtered compressed high dynamic range image data with only one sample per pixel. The high dynamic range image may be reconstructed from the low dynamic range format data and further processed as high dynamic range format data for a range of applications.