Orthogonal Function Image Compression for Bandwidth Reduction
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Solution Overview
Problem
Current image and video compression techniques require significant bandwidth for data transmission, as they involve transmitting large amounts of data to reconstruct images and videos, which is inefficient, especially for data-intensive applications like streaming video.
Innovation Solution
The system employs orthogonal functions, such as Hermite-Gaussian moments, to minimize the space-spatial frequency of image data, allowing for the transmission of compressed data by encoding and decoding coefficients, thereby reducing the amount of data needed for transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If present compression techniques are used to transmit image and video data, then data can be transmitted over wireless or wireline connections, but significant bandwidth is required and transmission efficiency is low
Solution Approach 1:
The patent extracts and transmits only the most significant coefficients from the image/video data after transformation, rather than transmitting all data. This selective extraction of essential information reduces bandwidth consumption while maintaining acceptable reconstruction quality, directly addressing the contradiction between bandwidth efficiency and transmission effectiveness
Solution Approach 2:
The patent transforms image/video data from spatial domain to frequency domain using orthogonal transformations, changing the representation parameters. This transformation reveals the energy distribution across different frequencies, allowing selective transmission of only significant coefficients, thereby improving transmission efficiency and reducing bandwidth requirements
2Quantity of substance
If subsets of data are transmitted and reconstructed at receiving location, then bandwidth is reduced, but reconstruction quality may be compromised
Solution Approach 1:
The patent performs preliminary orthogonal transformation and significance assessment at the transmitting end before data transmission. By pre-identifying and selecting the most significant coefficients for transmission, the system ensures that the subset of transmitted data contains the essential information needed for high-quality reconstruction, thus maintaining image quality while reducing data volume
Solution Approach 2:
The transformation to frequency domain changes the parameter representation of image data, allowing the system to work with coefficients that directly represent energy distribution. This parameter change enables selective transmission of significant coefficients while discarding or omitting less significant ones, achieving both data reduction and quality preservation
Data Source
AI summary
A system and method for transmitting compressed image data includes an encoder, responsive to received image data representing an image, for minimizing a space-spatial frequency of the image data by applying a predetermined orthogonal function thereto to generate a mathematical representation of the image data and extracting coefficients of the mathematical representation of the image data. A transmitter transmits the coefficients of the image data from a first location to a second location. A receiver receives the transmitted coefficients of the image data at the second location from the first location. A decoder recreates the mathematical representation of the image data at the second location responsive to the received coefficients and the predetermined orthogonal function and generates the image data from the recreated mathematical representation of the image data.