Area-Aware Adaptive Image Compression Using Dual- Background Classification for Optimized Data Preservation and Quality Enhancement

In the current era of data-intensive applications, including OTT services, IoT, and autonomous systems, efficient image compression is indispensable in order to minimize bandwidth consumption while preserving visual quality. Conventional compression techniques frequently fail to satisfactorily balance compression ratios and the preservation of critical image details. This work introduces a novel adaptive image compression method that is area-aware and utilizes a dual-background classification system to improve data preservation. The method classified areas into major and minor backgrounds by aggregating image regions based on visual characteristics. It applied aggressive quantization to less salient regions and refined compression techniques for critical details. A saliency map, reflecting human visual perception, guides this process, ensuring the preservation of the most visually significant information. By customizing quantification to the visual priorities of a particular region, the proposed methodology enhances the quality of images and the efficacy of compression. Extensive testing demonstrates that the area- aware approach outperforms traditional compression algorithms, enhancing the visual experience and significantly reducing data traffic. This research work mainly focuses on the reducing of data size and simultaneously preserving the image quality at required regions by using of adaptive compression techniques. This is particularly relevant for applications that necessitate high-quality image transmission in a data-driven world.