Image Processing Application Development: A New Approach and Its Economic Profitability

The paper presents a novel approach to image compression by leveraging linear approximation algorithms. As the digital data generated in various fields, such as medicine, telecommunications, and multimedia, continues to grow efficiently, managing high-quality images becomes crucial. Image compression has emerged as the primary method to reduce data while maintaining image quality. In this study, we explore the Reumann-Witkam and Douglas-Peucker algorithms to approximate the histogram of grayscale images due to their effectiveness in preserving essential image features during compression. The paper includes the development of a user-friendly interface and basic software functionality for implementing this approach. The significance of our work lies in resolving image compression challenges in different applications and improving the storage and processing of graphic data. We assess the tools for dockerization and integration of the CI/CD process into application development to enhance software implementation methods. This research has discovered new ways to improve image quality using less data, which can benefit industries relying heavily on images. By utilizing the Reumann-Witkam and Douglas-Peucker algorithms, we have achieved highly efficient image compression without sacrificing quality. The economic analysis of an online application was conducted, taking into account essential metrics such as ROI, payback period, net present value, and internal rate of return for a thorough evaluation. This paper contributes to developing image compression techniques, software, and economic viability in digital information technology.