Global Feature Transform Matrix for Image Encoding Efficiency
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to a significant increase in the amount of transmitted and stored data, resulting in higher transmission and storage costs, necessitating the development of efficient image compression technologies.
Innovation Solution
A feature information encoding/decoding method and apparatus that utilizes a global transform matrix for efficient encoding and decoding, allowing for the transmission and storage of bitstreams generated by these methods, which are then used to reconstruct images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality image data is transmitted and stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent extracts only the essential feature information from images using deep learning models, transforming complex high-resolution image data into compact feature representations. This extraction process separates critical visual information from redundant data, enabling quality preservation with reduced data transmission and storage requirements
Solution Approach 2:
The patent transforms image data from pixel-space to feature-space representation, changing the fundamental parameters from raw pixel values to extracted feature vectors. This parameter transformation maintains the essential information content while significantly reducing data dimensionality and volume for transmission and storage
2Productivity
If feature transform is performed using traditional methods, then encoding is completed, but encoding efficiency is limited
Solution Approach 1:
The patent performs feature extraction and transform matrix determination in advance during the encoding process. By pre-computing feature representations and transforming data before transmission, the system prepares optimized feature sets that reduce real-time processing requirements, thereby improving overall encoding efficiency without significant time loss
Solution Approach 2:
The patent introduces a deep learning-based feature extraction module as an intermediary between traditional encoding processes. This intermediary transforms raw image data into optimized feature representations that are more suitable for compression and transmission, bridging the gap between raw data and efficient encoding formats
Data Source
AI summary
A method and apparatus for encoding/decoding feature information of an image and a computer-readable recording medium generated by the encoding method are provided. The encoding method comprises obtaining at least one feature map for a first image, determining at least one feature transform matrix for the feature map, and transforming a plurality of features included in the feature map based on the determined feature transform matrix. The at least one feature transform matrix may comprise a global feature transform matrix commonly applied to two or more features, and the global feature transform matrix may be generated in advance based on a predetermined feature data set obtained from a second image.