Signal Dithering with Error-Based Transform for HDR Quantization
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Solution Overview
Problem
High precision signals, such as HDR, often require conversion to lower precision formats for output systems that cannot handle the original precision, resulting in information loss and suboptimal reproduction quality.
Innovation Solution
Applying an error-based transform to predict and uniformize quantization error, followed by stochastic dithering and subsequent reversal, allows for effective processing and representation of high precision signals in fewer bits, reducing computational and power requirements while maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If high precision signals are converted to lower precision formats for output systems, then the signal can be reproduced by systems with limited capabilities, but information is lost and reproduction quality deteriorates
Solution Approach 1:
The patent applies dithering before the quantization process to pre-randomize the signal. This preliminary action ensures that when quantization occurs, the error introduced is stochastic rather than systematic, preserving more perceptual information in the lower precision format and improving reproduction quality while maintaining compatibility with limited-capability output systems
Solution Approach 2:
The patent introduces dither noise as an intermediary element between the high precision input signal and the low precision quantized output. This intermediary randomizes the quantization error, allowing information to be effectively transferred across the precision gap while maintaining signal fidelity within the constraints of the output system's capabilities
2Reliability
If dithering is applied to non-uniform quantization error, then stochastic noise can be reduced, but the effectiveness of dithering is limited and computational resources are wasted
Solution Approach 1:
The patent performs error analysis and signal transformation before applying dithering to pre-condition the signal. By evaluating the quantization error characteristics in advance and transforming the signal accordingly, the patent ensures that dithering is applied optimally, maximizing its effectiveness while minimizing unnecessary computational expenditure
Solution Approach 2:
The patent employs error evaluation as a feedback mechanism to assess the quantization error and adjust the signal transformation accordingly. This feedback loop ensures that the signal is optimally prepared for dithering, allowing the system to adaptively optimize dithering effectiveness while avoiding waste of computational resources on suboptimal signal states
Data Source
AI summary
Systems, methods, and computer readable media are described for effectively using dither techniques upon signals having a predicted quantization error that varies across the range of the signal. In some embodiments, predicted error is used to shape a precision input signal so that the newly-shaped signal yields a uniform or relatively uniform predicted quantization error. A dither is applied to the re-shaped signal, and the shaping is reversed, after which the signal may be slope limited and/or quantized, taking full and efficient advantage of the dithering technique.