Compression-Aware Image Classification Using Deep Learning and SMTP-Based Multimedia Transmission

This project is aims to solves the problem associated with capturing, compressing, decoding, sending, uncompressing, decoding and displaying photographs of flowers. The size of the photos is decreased without severely impacting their quality using the DCT and quantization techniques of the JPEG compression algorithm. It is simpler to distribute and analyze the compressed photos remotely since the SMTP protocol is used to transport them across a network. In order to recognize and categorize flowers in the photos, the rendering process of the system makes use of computer vision and machine learning algorithms. To appropriately categorize the flowers, this technique entails classify the different types of flowers. In this project we used five categories. The system's capacity to identify and categorize flowers may be put to use in a variety of ways, including aiding in flower study or building an online database of flowers. Ultimately, this project shows how technology may be used to take and analyze flower photos, which can progress gardening, botany, and other related subjects. The project is a good prototype for students, researchers etc. to its accuracy, effectiveness, and user-friendly design.