AREA-AWARE ADAPTIVE IMAGE COMPRESSION SYSTEM USING DUAL BACKGROUND CLASSIFICATION AND SALIENCY-BASED QUANTIZATION

Abstract

Abstract: The present invention provides an innovative method for image compression that adapts to the visual significance of different regions in an image. Utilizing a dual-background classification approach, the system clusters image areas into major and minor backgrounds based on visual characteristics. These classifications guide an adaptive quantization strategy informed by saliency mapping, enabling optimal data preservation for critical visual information while enhancing compression efficiency. This method significantly reduces data traffic and maintains high image quality, making it ideal for data-intensive applications, such as OTT services, IoT, and autonomous systems.

Specification

Description:Area-Aware Adaptive Image Compression System Using Dual Background Classification and Saliency-Based Quantization
TECHNICAL FIELD OF THE INVENTION:
This invention relates to the field of image compression, particularly to adaptive image compression techniques that optimize bandwidth usage while preserving the quality of essential visual information. The invention is especially relevant for data-heavy applications where bandwidth efficiency and high-quality visual data are critical, such as OTT (Over-The-Top) streaming services, Internet of Things (IoT) devices, and autonomous systems.
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BACKGROUND OF THE INVENTION:
As data-intensive applications proliferate across various domains, the management of bandwidth and the preservation of image quality during transmission have emerged as significant technical challenges. This is particularly true in fields such as telemedicine, online gaming, video conferencing, and remote sensing, where high-quality visual data is essential for effective communication and analysis. The rapid growth in the volume of image and video data generated and shared necessitates innovative approaches to image compression that can efficiently handle the demands of modern technology.
Traditional image compression methods, such as JPEG and PNG, often struggle to strike a balance between achieving high compression ratios and retaining essential visual details. These methods typically apply uniform compression across an entire image, which can lead to the loss of critical information in areas that hold greater visual relevance. For instance, in a medical imaging context, a radiologist examining an X-ray or MRI scan needs to observe minute details in specific regions to make accurate diagnoses. If these crucial areas are overly compressed, vital information may be lost, potentially compromising patient care.
Moreover, traditional compression techniques often do not account for the varying importance of different image regions. In many applications, certain parts of an image may contain information that is far more significant than others. For example, in a scene depicting a crowded public space, the faces of individuals may carry more weight than the background. An effective compression algorithm should adapt to these priorities, preserving quality where it matters most while applying more aggressive compression to less significant areas.
This necessitates the development of adaptive image compression methods that can intelligently prioritize critical image areas. Such methods would dynamically analyze images to identify regions of interest and adjust compression levels accordingly. This adaptive approach could significantly reduce the overall data load transmitted without compromising the quality of the information that users need to extract from the images. For example, an adaptive algorithm could maintain high fidelity in the faces and other key objects in a photograph while applying more aggressive compression to the surrounding scenery, which is less critical to the viewer's understanding of the image.

Current methods for image compression often lack the necessary optimization to respond dynamically to varying visual priorities across different regions of an image. This is particularly evident in real-time applications, where the need for speed and efficiency is paramount. In scenarios such as live video streaming or telemedicine consultations, the ability to maintain high image quality while minimizing data transmission is crucial. Delays or degradations in image quality can disrupt communication and impact user experience, making it essential to develop solutions that can adapt to these challenges on-the-fly.
In addition to enhancing user experience, adaptive compression methods have the potential to alleviate the strain on network bandwidth. As more devices connect to the internet and demand for data increases, network congestion can lead to slower transmission speeds and reduced image quality. By employing intelligent compression strategies that prioritize important image features, it is possible to optimize bandwidth usage, ensuring that networks can handle larger volumes of data more efficiently.
Furthermore, the integration of machine learning techniques into image compression algorithms presents an exciting avenue for future development. Machine learning models can be trained to recognize patterns in images and predict which areas are most important based on context. By leveraging this technology, adaptive compression methods could become even more sophisticated, learning from user interactions and preferences over time. This could lead to further enhancements in compression efficiency and image quality, creating a more seamless experience across various applications.
In conclusion, as the demand for high-quality image transmission continues to grow, the development of adaptive image compression methods becomes increasingly important. Traditional approaches are insufficient to meet the nuanced needs of modern applications, which require a balance between data efficiency and visual integrity. By prioritizing critical image areas and employing intelligent, dynamic strategies, it is possible to achieve significant improvements in both bandwidth management and image quality. The future of image compression lies in innovative solutions that not only respond to the needs of users but also anticipate them, paving the way for a more connected and visually rich digital landscape.________________________________________
OBJECTS OF THE INVENTION:
1. To provide an image compression method that balances high compression ratios with the preservation of essential image details.
2. To develop an adaptive compression system that prioritizes different image areas based on their visual significance, as perceived by the human visual system.
3. To reduce data traffic in applications requiring high-quality image transmission without significant quality loss.
4. To leverage saliency mapping in image compression to optimize quantization based on human perception of visual importance.
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SUMMARY OF THE INVENTION:
The field of image compression has witnessed significant advancements in recent years, driven by the increasing demand for efficient storage and transmission of visual data. However, traditional methods often apply uniform compression techniques across entire images, leading to a loss of critical details, especially in regions that are visually significant to human observers. This invention addresses these limitations by introducing an innovative adaptive image compression method that employs a dual-background classification system. This system intelligently differentiates between various regions of an image based on their visual importance, applying tailored compression techniques that optimize both image quality and file size.
At the core of this invention lies the dual-background classification system, which categorizes image regions into two distinct classes: "major" backgrounds and "minor" backgrounds. The major background areas, typically less critical to the overall visual experience, undergo aggressive quantization. Quantization, a key step in image compression, reduces the number of distinct colors or shades in an image, which can significantly decrease file size. By applying this technique more heavily to the major background areas, the method effectively reduces data storage requirements without sacrificing the perceived quality of the image in the less critical regions. In contrast, the minor background areas contain essential details or visual elements crucial for viewer perception. These regions are compressed using more conservative techniques that prioritize the preservation of detail and clarity. This differential approach allows the system to maintain the integrity of significant visual information, ensuring that important features are not lost or degraded during the compression process.
A vital component of the dual-background classification system is the utilization of a saliency map, which serves as a visual guide highlighting areas of an image that are likely to attract human attention. By analyzing this saliency map, the system can determine which parts of the image should be classified as minor backgrounds, requiring more careful compression. This analysis is grounded in the principles of human visual perception, suggesting that certain features, such as edges, contrasts, and colors, are more likely to be noticed by viewers. The saliency map is generated using advanced algorithms that consider various factors, including color contrast, spatial frequency, and object boundaries. By integrating these elements, the saliency map effectively identifies regions that are critical to the viewer's experience, allowing the adaptive compression method to prioritize these areas. This ensures that the final compressed image retains its visual fidelity, particularly in parts essential for conveying information or emotional impact.
The adaptive image compression method introduced by this invention offers several significant advantages over traditional compression techniques. First and foremost, it enhances compression efficiency by intelligently allocating resources based on visual priority. This targeted approach not only reduces the overall file size but also ensures that the most important visual elements are preserved, resulting in a higher quality image. Furthermore, by aligning the compression process with human visual perception, the invention minimizes the potential for noticeable artifacts or distortions that can occur with standard compression methods. Viewers are less likely to perceive quality degradation in key areas, leading to a more satisfying visual experience. This is particularly beneficial in applications where image quality is paramount, such as in photography, medical imaging, and digital media.
In conclusion, the invention of an adaptive image compression method utilizing a dual-background classification system represents a significant advancement in the field of image processing. By applying differentiated compression techniques based on visual priority, the system effectively balances the need for reduced file sizes with the preservation of critical visual information. The integration of a saliency map further enhances the system's ability to align compression efforts with human perception, making it a powerful tool for improving the efficiency and effectiveness of image compression. As digital content continues to proliferate across various platforms, the need for innovative solutions like this will only grow, paving the way for enhanced user experiences and optimized data management.________________________________________
Drawing with Title & Label Description

Fig-1. Comprehensive Workflow for Adaptive Image Compression Based on Visual Importance and Background Classification

• Block 101: Image Input - The original image to be compressed is received by the system.
• Block 102: Image Clustering Module - The image is analyzed, and regions are clustered into major and minor backgrounds based on visual characteristics.
• Block 103: Saliency Map Generator - A saliency map is generated to highlight regions with high visual importance, based on human perception.
• Block 104: Dual Background Classification - Using the clustering and saliency map, image areas are classified as either major or minor backgrounds.
• Block 105: Adaptive Quantization Module - Compression parameters are assigned based on background classification, applying higher quantization to major backgrounds and refined quantization to minor backgrounds.
• Block 106: Compressed Image Output - The final compressed image is generated, with different compression levels applied to major and minor areas.
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BRIEF DESCRIPTION OF THE DRAWING:
The drawing illustrates the steps involved in the area-aware adaptive image compression method. It starts with image input (Block 101), followed by image clustering (Block 102), saliency map generation (Block 103), and dual background classification (Block 104). Adaptive quantization is then applied (Block 105) based on the classified areas, and the compressed image is outputted (Block 106).
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DETAILED DESCRIPTION OF THE DRAWING:
1. Image Input (Block 101): The process begins with receiving an image, intended for compression, into the system. This image can be sourced from various applications, including real-time video feeds, static images, or streaming content.
2. Image Clustering Module (Block 102): The system analyzes the image and segments it into distinct regions based on visual characteristics such as color, texture, and edges. These regions are prepared for classification based on their significance.
3. Saliency Map Generator (Block 103): A saliency map is generated to assess the visual importance of different image regions. This map is constructed to reflect areas that align with human visual perception, assigning higher values to regions deemed critical for visual quality.
4. Dual Background Classification (Block 104): Using the saliency map and clustering results, image areas are classified into "major background" (less significant) and "minor background" (more visually significant) categories. This classification is critical to assigning the correct compression strategy for each area.
5. Adaptive Quantization Module (Block 105): Quantization values are applied adaptively based on the classification from Block 104. Major background areas undergo aggressive quantization, reducing data significantly, while minor background areas are subjected to refined compression to preserve critical visual details.
6. Compressed Image Output (Block 106): The compressed image is outputted with region-specific compression adjustments. This image is ready for transmission, offering optimized data usage without compromising on quality in areas of visual importance.
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ADVANTAGES OF THE INVENTION:
1. Enhanced Compression Efficiency: By applying differential compression based on visual importance, the system achieves high compression ratios without sacrificing critical image quality.
2. Data Traffic Reduction: The adaptive quantization technique significantly reduces data traffic, making the system ideal for bandwidth-sensitive applications.
3. Preservation of Visual Quality: By aligning compression with human visual perception, the system ensures that essential details remain clear, enhancing the end-user experience.
4. Real-Time Adaptability: The approach can be adapted for real-time applications, making it suitable for video streaming and other dynamic image-transmission services.
, Claims:Claim 1: An adaptive image compression method comprising:
• Receiving an image for compression: The system initiates the process by obtaining an input image that requires compression, which may include still images or video frames.
• Clustering image regions based on visual characteristics: The method includes a step where the image is analyzed and divided into distinct regions based on various visual properties, such as color, texture, and contrast. This clustering is essential for understanding the visual composition of the image.
• Generating a saliency map to evaluate the visual significance of regions: A saliency map is created that assesses which regions of the image are more likely to attract human attention. This map is pivotal in informing the subsequent classification of regions based on human visual perception.
• Classifying image regions into major and minor backgrounds: Based on the clustering and the generated saliency map, regions are categorized into major backgrounds, which are visually less significant, and minor backgrounds, which may contain critical details. This classification allows for tailored compression strategies for different areas of the image.
• Applying a first compression method to the major background regions with higher quantization and a second, more refined compression method to the minor background regions: The system employs aggressive quantization techniques on the major background areas, significantly reducing their data size, while applying more conservative compression methods to the minor background areas to preserve important visual details.
• Outputting a compressed image optimized for data reduction and quality retention: The result of this process is a compressed image that effectively reduces data traffic while maintaining the quality of crucial visual information, suitable for transmission or storage.
• Claim 2: The method of Claim 1, wherein the saliency map reflects human perception-based visual priority, guiding adaptive quantization to prioritize quality retention in critical image areas. This claim emphasizes the method's reliance on a saliency map that is specifically designed to mimic human visual focus, ensuring that the most attention-grabbing areas of the image receive the best possible quality during compression.
• Claim 3: The method of Claim 1, further comprising a dual-background classification system that optimizes quantization values specific to the visual priority of each region. This claim highlights the innovative aspect of the dual-background classification, which enhances the efficiency of the quantization process by allowing the system to assign tailored quantization levels based on the specific visual importance of each classified region.
• Claim 4: An image compression system, comprising:
• A module for receiving an image: This module acts as the interface for inputting images into the system.
• An image clustering module to segment image regions based on visual characteristics: This component performs the initial analysis of the image to identify and segment its various regions based on distinct visual traits.
• A saliency map generator that highlights regions of visual significance: This element of the system produces a saliency map that delineates which parts of the image are likely to be perceived as most important by viewers.
• A classification module for identifying major and minor background areas based on clustering and saliency data: This module processes the output from the clustering and saliency map generation to categorize image regions into major and minor backgrounds.
• An adaptive quantization module to assign compression levels specific to each classified region: This component applies different levels of compression to the classified regions, enabling the optimization of data reduction while preserving visual quality.
• Claim 5: The method of Claim 1, wherein the clustering of image regions employs machine learning techniques to enhance the accuracy of classification. This claim introduces the possibility of using advanced machine learning algorithms to refine the clustering process, potentially leading to better segmentation and more accurate identification of visual significance in image regions.
• Claim 6: The method of Claim 1, wherein the adaptive quantization method employs feedback from prior compression sessions to iteratively improve the compression efficiency and image quality over time. This claim emphasizes the dynamic learning capability of the system, allowing it to adapt based on past performance and continually optimize its compression strategy.
• Claim 7: An application of the method of Claim 1 in data-intensive environments, such as OTT (Over-The-Top) streaming services, IoT (Internet of Things) applications, or autonomous systems, where maintaining high image quality during compression is critical. This claim asserts the applicability of the proposed method in practical, real-world scenarios, highlighting its relevance for industries that demand high-quality image delivery with efficient data management.

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