Invertible Linear Mapping with Autoregressive Convolution for Data Processing
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Solution Overview
Problem
Existing digital image, audio, and video processing technologies face limitations in performance for enhancement, transmission, and storage, particularly in image transformation, recognition, anomaly detection, and control of autonomous vehicles, due to restrictive data processing structures and inefficiencies in generative modeling methods.
Innovation Solution
The implementation of a computer-implemented method using invertible linear mappings with autoregressive convolutions for digital image, audio, and video data processing, allowing for efficient transformation, encoding, and decoding, and enabling improved performance in image recognition, anomaly detection, and control of autonomous systems through sequential and parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional generative modeling methods are used, then implementation is straightforward, but processing efficiency and performance are limited
Solution Approach 1:
The patent segments the data processing into distinct stages: encoding through invertible mapping, modification in latent space, and decoding through inversion. This segmentation allows each stage to be optimized independently, improving overall processing efficiency while managing complexity through modular architecture.
Solution Approach 2:
The patent transforms data from pixel space to a different representation space through invertible linear mapping, enabling processing in a transformed dimension where modifications can be made more efficiently. This dimensional transformation allows complex image processing operations to be performed more effectively in the latent space.
2Manufacturing precision
If complex processing structures are implemented to improve performance, then image enhancement quality improves, but computational complexity increases
Solution Approach 1:
The patent introduces an intermediate latent representation space that mediates between the input image and the enhanced output. This intermediary space allows complex transformations to be decomposed into simpler operations: encoding to latent space, modification, and decoding back to image space, thereby improving quality while managing computational complexity.
Solution Approach 2:
The patent replaces traditional mechanical image processing operations with learnable neural network-based invertible mappings. This substitution allows the system to achieve high-quality enhancement through data-driven transformations rather than fixed algorithmic operations, improving performance while providing flexibility in complexity management.
3Productivity
If invertible linear mapping with autoregressive convolution is used, then data processing efficiency is enhanced, but implementation complexity increases
Solution Approach 1:
The patent applies preliminary encoding through invertible linear mapping with autoregressive convolution before modification. This preliminary transformation organizes the data into an efficient representation that facilitates subsequent processing operations, improving overall efficiency while the complexity is confined to the initial encoding stage.
Data Source
AI summary
Computer implemented method for digital image data, digital video data or digital audio data enhancement, and a computer implemented method for encoding or decoding this data in particular for transmission or storage, wherein an element representing a part of said digital data comprises an indication of a position of the element in an ordered input data of a plurality of data elements, wherein a plurality of elements is transformed to a representation depending on an invertible linear mapping, wherein the invertible linear mapping maps the input of the plurality of elements to the representation, wherein the invertible linear mapping comprises at least one autoregressive convolution.