AI-Powered Image Compression Algorithm for Efficient Transmission

Abstract

The invention provides an AI-powered image compression method and system that utilizes deep learning techniques to optimize image data compression for efficient transmission. By analyzing image content through a trained AI model, the system selectively applies varying compression levels to different regions of the image based on their importance, preserving critical details while reducing file size. This approach enables efficient transmission of compressed images over networks with varying bandwidth, ensuring minimal latency and reduced data consumption without compromising visual quality. The invention is applicable in fields such as real-time video streaming, medical imaging, and remote sensing.

Specification

Description:The present invention relates to the field of image compression techniques, specifically to a method and system that leverages artificial intelligence (AI) for enhanced image compression and transmission efficiency. More particularly, the invention focuses on utilizing AI-driven deep learning algorithms to adaptively compress image data for optimal transmission over networks, reducing bandwidth consumption while maintaining high visual quality.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Current Image Compression Techniques: Traditional image compression techniques, such as JPEG, PNG, and GIF, are , Claims:1. A method for compressing image data using artificial intelligence (AI) to enhance transmission efficiency, comprising the steps of:
analyzing the image content using an AI model to identify different regions based on features such as detail, texture, and color;
applying varying compression levels to the identified regions of the image to achieve a reduced file size, wherein regions with higher details are compressed less aggressively and regions with lower details are compressed more aggressively;
encoding the compressed image data for transmission over a network;
decoding the compressed image data at a receiving end using a corresponding AI model to restore the image as closely as possible to its original state.

2. The method of claim 1, wherein the AI model used for analysis is a convolutional neural network (CNN) trained on a large dataset of images to identify patterns that optimize compression strategies.

3. The method of claim 1, wherein the transmission is performed over a network with varying ban

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