AI-BASED IMAGE COMPRESSION SYSTEM WITH ADAPTIVE GENERATIVE NEURAL NETWORKS FOR IMAGE ENHANCEMENT IN LOW-LIGHT CONDITIONS

Abstract

[033] This invention presents an advanced image and video compression system that combines hybrid neural networks (Variational Autoencoders, Generative Adversarial Networks, and Transformers) with quantum computing and edge computing for enhanced efficiency and scalability. The system compresses data by encoding it into a latent space, reconstructing high-quality images, and capturing dependencies across video frames. Quantum processors handle intensive computations, while edge computing facilitates real-time compression closer to data sources. Auxiliary data and meta-learning optimize compression for varying content, and a reinforcement learning agent ensures adaptive data flow in fluctuating network conditions. This system is suited for applications requiring high-quality, low-latency compression, such as streaming, telemedicine, and AR/VR. Accompanied Drawing [FIG. 1]

Specification

Description:[018] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[019] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[020] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
This invention presents an advanced image and video compression system that combines hybrid neural networks (Variational Autoencoders, Generative Adversarial Networks, and Transformers) with quantum computing and edge computing for enhanced efficiency and scalability. The system compresses data by encoding it into a latent space, reconstructing high-quality images, and capturing dependencies across video frames. Quantum processors handle intensive computations, while edge computing facilitates real-time compression closer to data sources. Auxiliary data and meta-learning optimize compression for varying content, and a reinforcement learning agent ensures adaptive data flow in fluctuating network conditions. This system is suited for applications requiring high-quality, low-latency compression, such as streaming, telemedicine, and AR/VR.
System Overview
[021] Hybrid Neural Network Processing of Image Data
The hybrid neural network architecture forms the backbone of this invention, employing VAEs, GANs, and Transformers in tandem. The VAE compresses the image data into a latent representation, optimizing the encoding process by reducing redundancy. GANs reconstruct the image data from this latent space with minimal quality loss, while Transformers track dependencies between frames in video sequences, ensuring consistency and temporal coherence. This hybrid approach enables high compression ratios while preserving essential details and minimizing distortions.
[022] Quantum Computing for Accelerated Compression
Quantum processors handle the most computationally demanding aspects of the compression process, such as encoding high-dimensional feature representations and complex mathematical transformations. Quantum-assisted algorithms ensure faster processing times by leveraging quantum parallelism, which enhances scalability for high-volume image and video data processing.
In applications where latency is critical, such as live streaming and video conferencing, quantum computing allows the compression system to operate with near-zero delays, enhancing the user experience and maintaining high image quality even at low bitrates.
[023] Edge Computing and Real-Time Decentralized Compression
Edge computing capabilities are embedded within the system, allowing compression tasks to be distributed to devices within proximity to the data source (e.g., cameras or smartphones). This setup reduces the need to send large volumes of data to centralized servers, effectively lowering latency and bandwidth usage. Edge computing also enhances security by keeping data processing local, reducing the risk of data breaches.
[024] Auxiliary Data Utilization and Meta-Learning for Contextual Compression
Auxiliary data is integrated into the compression model to refine processing based on the content characteristics. Meta-learning algorithms adjust the model's parameters dynamically by analyzing contextual information, such as motion intensity, scene changes, and object density. For instance, in a low-motion scene, the compression model might prioritize data fidelity, while in high-motion scenes, it might adjust parameters to focus on maintaining temporal consistency.
[025] This adaptable approach enables the model to function optimally across various applications, from low-motion video calls to high-motion sports streaming, tailoring the compression strategy to the specific requirements of each scenario.
[026] Reinforcement Learning for Predictive Data Management
Reinforcement Learning (RL) is applied to manage encoding parameters and adapt to changing conditions. By simulating different scenarios and learning from the outcomes, the RL agent can predict the most efficient compression configurations based on factors like network conditions, user preferences, and content type.
[027] This adaptability is particularly beneficial for applications in environments with fluctuating bandwidth, such as mobile streaming. The system learns to balance quality and compression, adjusting parameters in real time to deliver the best possible visual experience under the available conditions.
[028] Applications of the Invention
The AI-powered compression system has versatile applications in industries where efficient image and video compression is critical:
Streaming Services: Streaming platforms benefit from high compression ratios without quality compromise, providing a better viewing experience for users on limited bandwidth connections.
Telemedicine and Remote Diagnostics: By compressing medical imaging data with minimal quality loss, this technology facilitates faster, secure transmission for remote diagnostics and telemedicine applications.
Surveillance and Smart Cities: Real-time compression at the edge reduces data load for surveillance networks, enabling high-resolution, low-latency monitoring in smart city environments.
Augmented Reality (AR) and Virtual Reality (VR): The low-latency processing enabled by quantum and edge computing enhances AR/VR applications, delivering smoother visuals by compressing data in real-time without sacrificing detail.
Technical Advantages of the Invention
Enhanced Compression Ratios with Minimal Quality Loss: The combination of VAEs, GANs, and Transformers achieves high compression efficiency while preserving image and video quality.
Scalability and Speed via Quantum Computing: Quantum processing accelerates computation, making the system highly scalable and suitable for large datasets.
Real-Time Processing with Edge Computing: Edge deployment minimizes latency, ensuring smooth performance in time-sensitive applications.
Dynamic Adaptability through Reinforcement Learning: Predictive RL algorithms ensure the compression model adapts to changing conditions, optimizing performance in real-world scenarios.
Improved Security and Privacy: With edge processing, sensitive data is processed locally, reducing the need for extensive data transfer and enhancing security.
[029] Future Developments
As the AI and quantum computing fields continue to evolve, this system can be expanded to incorporate:
Advanced Quantum Algorithms: Further enhancing speed and efficiency for high-dimensional data processing.
Extended Edge Computing Capabilities: Enabling complex tasks to be executed across distributed networks with minimal central server dependence.
AI-Driven Content Analysis: Implementing deeper learning models to automatically detect and adjust for various content types and characteristics, refining compression even further.
[030] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[031] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[032] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention. , Claims:1.A hybrid neural network-based compression system comprising: A Variational Autoencoder for encoding input data into a reduced-dimensional latent space; A Generative Adversarial Network for refining the decompressed output to minimize artifacts; and A Transformer network to capture temporal and spatial dependencies in sequential data for enhanced video compression.

2.The system of Claim 1, further comprising a quantum computing module for performing high-dimensional encoding computations to accelerate processing time and scalability.

3.The system of Claim 1, wherein the compression tasks are distributed to edge devices, enabling real-time compression at data sources to reduce network latency and bandwidth usage.

4.The system of Claim 1, further comprising a meta-learning module that adapts compression parameters based on auxiliary data, including motion vectors and scene descriptors, for content-aware compression.

5.The system of Claim 1, wherein a reinforcement learning agent is employed to predict and adjust encoding parameters dynamically based on network conditions and user preferences, maintaining optimal data flow.

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