High Dynamic Range Data Decompression Using Endpoint Indices
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Solution Overview
Problem
Conventional compression techniques do not effectively support high dynamic range data, which requires more than 16 bits per pixel, leading to increased memory bandwidth requirements and inefficiencies in processing high-quality images.
Innovation Solution
A system and method for decompressing high dynamic range data using fixed-size block compression formats with 8 bits per pixel, employing two or four compressed endpoint values and indices, with mode bits to distinguish between formats and specify endpoint compression modes, allowing for efficient hardware decompression and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression techniques (e.g., DXTC 565 format) are used to compress pixel data, then memory bandwidth is reduced compared to uncompressed data, but high dynamic range data (48+ bpp) cannot be effectively supported and visual quality is compromised
Solution Approach 1:
The patent segments the pixel data into compressed endpoint values and indices, where endpoint values represent color space boundaries and indices map to these endpoints. This segmentation allows efficient compression of high dynamic range data by representing the full dynamic range through a limited set of compressed endpoints rather than storing every pixel value at full precision.
Solution Approach 2:
The patent changes the parameter representation from storing full precision pixel values (48+ bpp) to storing compressed endpoint values (8 bpp) with associated indices. This parameter transformation enables the system to maintain high dynamic range information while using significantly less memory bandwidth, as the compressed format stores only the essential endpoint definitions and lookup indices.
2Measurement precision
If high dynamic range data is stored in uncompressed format (48+ bits per pixel), then visual quality is maintained, but memory bandwidth requirements increase significantly
Solution Approach 1:
The patent creates a compressed copy of the high dynamic range data that preserves the essential visual information. Instead of storing the full precision pixel values directly, the system stores compressed endpoint values and indices, which act as a condensed representation that can be efficiently decompressed to retrieve the original luminance values when needed.
Solution Approach 2:
The patent performs preliminary compression of the high dynamic range data into a compact format before storage. By pre-compressing the data into endpoint values and indices, the system reduces memory bandwidth requirements during storage and transmission, while maintaining the ability to reconstruct the full precision data when required for processing or display.
3Quantity of substance
If compressed formats with fixed size blocks are used for high dynamic range data, then storage efficiency is improved, but hardware decompression capability becomes more complex
Solution Approach 1:
The patent implements dynamic decompression logic that can handle multiple compression modes (single endpoint pair and dual endpoint pair) within the same hardware architecture. The system dynamically selects the appropriate decompression path based on the compressed data format, allowing efficient hardware implementation without requiring separate dedicated hardware for each compression variant.
Solution Approach 2:
The patent designs a universal decompression framework that can handle both single endpoint pair and dual endpoint pair compression formats using the same hardware resources. The endpoint computation unit and pixel value computation unit are designed to be multi-functional, capable of processing different compression modes through configurable logic rather than requiring separate dedicated circuits for each format.
Data Source
AI summary
Systems and methods for representing high dynamic range data in compressed formats with a fixed size block allow high dynamic range data to be stored in less memory. The compressed formats use 8 bits per pixel. A first compressed format includes two endpoint values and an index for each pixel in the block. A second compressed format includes four endpoint values, a partition index that specifies a mask for each pair of the four endpoint values, and an index for each pixel in the block. The two formats may be used for various blocks within a single compressed image and mode bits are included to distinguish between the two formats. Furthermore, each endpoint value may be encoded using an endpoint compression mode that is also specified by the mode bits. Compressed high dynamic range values represented in either format may be efficiently decompressed in hardware.