Image Transform Subsampling and Interpolation
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Solution Overview
Problem
Existing image transform systems face inefficiencies due to the complexity and computational requirements of reverse transforming all output pixel addresses, which can lead to increased hardware needs and power consumption.
Innovation Solution
The method involves reverse transforming a subset of output pixel addresses and interpolating the remaining addresses to create an approximately transformed output, reducing the complexity and computational load, allowing for a more efficient image transform process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all output pixel addresses are reverse transformed to ensure accurate image transform, then image quality is maintained, but computational complexity and hardware requirements increase
Solution Approach 1:
The patent segments the set of output pixel addresses into two subsets: a first subset that is reverse transformed to generate accurate input pixel addresses, and a second subset that is interpolated from the first subset. This segmentation allows the system to process only critical addresses through the computationally intensive reverse transformation while deriving other addresses through simpler interpolation operations, thereby reducing hardware complexity while maintaining image quality.
Solution Approach 2:
The patent applies partial action by reverse transforming only a portion (first subset) of the output pixel addresses rather than all addresses. The remaining addresses (second subset) are obtained through interpolation based on the transformed addresses from the first subset. This partial transformation approach reduces computational complexity and hardware requirements while still achieving sufficient accuracy for the overall image transform.
2Manufacturing precision
If all output pixel addresses are reverse transformed to ensure accuracy, then transform precision is maintained, but computational load increases
Solution Approach 1:
The patent divides output pixel addresses into two groups: those in the first subset undergo full reverse transformation to ensure precision, while those in the second subset are derived through interpolation. This segmentation maintains transform precision for critical addresses while improving overall computational efficiency by avoiding redundant full transformations.
Solution Approach 2:
The system performs reverse transformation on only a partial subset of output pixel addresses rather than all addresses. The remaining addresses are computed through interpolation operations that are computationally less intensive. This partial transformation strategy maintains sufficient transform precision while significantly improving computational efficiency and reducing processing time.
3Productivity
If a subset of output pixel addresses is reverse transformed and others are interpolated, then computational efficiency improves, but some accuracy is lost
Solution Approach 1:
The patent strategically segments output pixel addresses into a first subset (reverse transformed for high accuracy) and a second subset (interpolated for efficiency). By carefully selecting which addresses fall into each subset, the system optimizes the balance between computational efficiency and address accuracy, ensuring that critical addresses maintain high precision while non-critical addresses achieve sufficient accuracy through interpolation.
Data Source
AI summary
Image transforms that can be performed through a reverse lookup process in which output pixel addresses of an output image are reverse transformed into input pixel addresses. In some embodiments, rather than reverse transforming all output pixel addresses, a subset of output pixel addresses can be reverse transformed to calculate transformed addresses. The reverse transforms of all output pixel address may then be approximately calculated by interpolating the transformed addresses. The approximately transformed addresses can then be used as read addresses to a memory storing the input image.