Digital Gradient Signal Processing for Multi-Dimensional Image Detail
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and enhancing the detail of high-definition video and image data, particularly in multi-processor systems, where data transmission and processing lag behind increasing data quantities, and user interfaces need to become more intuitive, especially for immersive experiences like virtual and augmented reality.
Innovation Solution
An adaptive multiprocessor computing system that includes an automatic code generator optimizing code for parallel processing, a digital signal processing module for real-time enhancement of image detail, and a natural language interface, along with a web page content converter and data transformer, to manage and compress data efficiently and improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data transmission and processing are increased to keep up with higher detail data streams, then image detail and quality are improved, but data transmission bandwidth and processing capacity requirements increase
Solution Approach 1:
The patent segments the image processing task by separating gradient computation for different dimensions. The system computes gradient signals for primary dimensions (e.g., horizontal and vertical) separately from secondary dimensions, allowing staged processing that reduces peak bandwidth requirements while maintaining overall image detail quality.
Solution Approach 2:
The patent performs preliminary gradient computations for primary dimensions before final image reconstruction. By pre-computing these gradient signals and storing them in intermediate buffers, the system reduces the real-time processing burden and allows for more efficient data transmission pipelines.
2Productivity
If multi-processor systems are used to increase processing capacity, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent divides the multi-processor workload by assigning different dimension gradient computations to different processors. One processor handles primary dimension gradients while another handles secondary dimension gradients, reducing inter-processor communication overhead and simplifying the coordination complexity.
Solution Approach 2:
The patent designs a universal gradient computation framework that can be implemented across multiple processors with the same algorithmic approach. This standardized methodology allows processors to be used interchangeably for different gradient computations, reducing the complexity of processor-specific optimizations and simplifying system architecture.
3Manufacturing precision
If gradient signals for all dimensions are computed simultaneously, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent segments gradient computation into sequential stages: first computing gradients for primary dimensions, then using those results to compute gradients for secondary dimensions. This staged approach maintains comprehensive gradient information for high-quality image reconstruction while avoiding the simultaneous computation bottleneck.
Solution Approach 2:
The patent computes primary dimension gradient signals as preliminary data before computing secondary dimension gradients. This preliminary computation allows secondary gradient calculations to leverage already-processed data, reducing redundant computations and overall processing time while maintaining complete gradient information for image quality.
Data Source
AI summary
A system and method for improving the detail of a digital signal comprising at least three dimensions can be implemented by extracting a plurality of data cubes containing two x-planes, two y-planes, two z-planes, and amplitude information at eight locations in this x, y, and z space. A primary and secondary direction and a data plane for each data cube can then be selected based on difference calculations of eight locations in the x, y, and z directions, resulting in a 2×2 data square. This data square can then be used to compute a network neighborhood, which can subsequently be used to calculate first and second order gradient information. The first and second order gradient information can be used to construct an output signal that has greater detail than the input signal.