Perceptual Processing Hardware Using Jacobian Mapping for Quality Metrics
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Solution Overview
Problem
Current methods for lossy data compression, particularly in image and video compression, struggle to accurately measure the perceived quality of compressed stimuli without relying on human evaluations, as existing metrics like RMS and PSNR fail to capture the complexities of human visual perception.
Innovation Solution
A novel system that uses hardware to accelerate the mapping of inputs to a Riemannian space using Jacobian matrices, enabling efficient perceptual processing and comparison of distances, thereby improving real-time performance and power efficiency in applications like real-time robots and augmented reality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional compression metrics (RMS, PSNR) are used to measure quality, then computational simplicity is maintained, but measurement precision deteriorates because they fail to capture human visual perception
Solution Approach 1:
The patent introduces Jacobian matrices as an intermediary computational tool that bridges the gap between simple pixel-wise operations and complex perceptual modeling. The Jacobian captures local linear transformations in perceptual space, enabling accurate quality measurement without requiring full-blown neural network computations at every step.
Solution Approach 2:
The patent transforms the problem from operating in raw pixel space to operating in perceptual space by applying parameter transformations through Jacobian matrices. This changes the mathematical parameters from simple intensity differences to perceptually-relevant differential transformations, improving measurement accuracy while maintaining computational tractability.
2Measurement precision
If complex perceptual modeling is implemented to accurately measure quality, then measurement precision improves, but use of energy increases due to computational demands
Solution Approach 1:
The patent segments the complex perceptual modeling task into localized computations at each pixel position, where Jacobian matrices capture only the immediate neighborhood transformations. This segmentation avoids global computations while maintaining perceptual accuracy, significantly reducing energy consumption.
Solution Approach 2:
The patent uses Jacobian matrices as simplified copies or approximations of full perceptual processing. Rather than implementing complete neural network models, the Jacobian provides a computationally-light copy that captures the essential perceptual transformations needed for quality assessment.
3Productivity
If real-time processing is achieved through hardware acceleration, then productivity improves, but device complexity increases due to specialized hardware requirements
Solution Approach 1:
The patent designs the hardware architecture to perform multiple functions: the same Jacobian computation infrastructure serves both compression quality assessment and perceptual rendering tasks. This multi-functionality achieves real-time processing without requiring separate specialized hardware for each function.
Data Source
AI summary
An autonomous module for processing stored data includes a multithreaded processor core (MPC) and a plurality of autonomous memories. Each of the plurality of autonomous memories has a memory bank, a data operator (DO) configured to implement a plurality of selectable memory behaviors, an autonomous memory operator (AMO) configured to implement a state machine to control the memory bank independently of the MPC, and at least one memory input/output (IO) port communicatively coupled with the memory bank, the AMO, and the DO. The at least one memory IO port is configured to receive a read instruction from the AMO, retrieve data from the memory bank, and send the data to the DO. The DO is configured to implement one of the plurality of selectable memory behaviors to update the data and send the updated data to the AMO via the at least one memory IO port.