PowerPoint Presentation

OUTLINE. Abstract Problem Statement System Design/Architecture System Development Approach (Technology Used) Algorithm & Deployment Conclusion Future Scope References Video of the Project.

Abstract. The project aims to develop an image compression website that utilizes autoencoder-based techniques to reduce image size while maintaining high image quality. Autoencoders are deep learning models that can efficiently learn representations of input data, making them suitable for image compression tasks. The website will allow users to upload images for compression, apply autoencoder-based compression techniques, and download the compressed images. The project targets improved website performance by reducing image file sizes without significant loss in image quality. Compression reduces the amount of space required to store data on disks, making it possible to store more data in the same physical space. Compressing executable files can reduce their size, making them quicker to download and install. Compressing large datasets used for training models can reduce storage requirements and speed up data loading times..

[Audio] "Next we will examine the OptiPict project and its unique method for compressing images. As we are aware high-resolution images can consume large amounts of storage and bandwidth. Traditional compression techniques often focus on preserving image quality which can limit the level of compression achieved. However in many situations preserving key features is more important than maintaining visual quality. This is where the OptiPict system becomes useful. By utilizing an autoencoder-based approach it prioritizes retaining essential features over image quality allowing for greater compression ratios. The system consists of a frontend Next.js Docker container and a backend Flask A-P-I Docker container with an autoencoder model at its core. This seamless integration enables efficient and lossy image compression with a user-friendly browser interface. Let's now proceed to the next slide to see how these components work together to create a powerful compression system..

[Audio] Slide number five delves into the system architecture of our project OptiPict. For the user interface we have opted for a browser for convenient access. On the technical side the frontend is supported by Next.js and containerized with Docker. Likewise the backend runs on Flask A-P-I also containerized with Docker. This design enables efficient and seamless image compression through autoencoder technology. With this system architecture we have full confidence in the success of OptiPict. Next in slide number six we will explore the details of our user interface and frontend technology..

[Audio] Today we are discussing the exciting project OptiPict which aims to revolutionize image compression through cutting-edge technology. Moving to slide number 6 we will be delving into the system deployment approach of this project. The core of OptiPict's image compression is the use of autoencoder a neural network architecture that allows for efficient representations of input data specifically images. The model is implemented and trained using deep learning frameworks like TensorFlow along with image processing libraries such as OpenCV for preprocessing tasks. Flask handles the backend logic and A-P-I endpoints while platforms like Azure are used for deployment making the web application accessible to users over the internet. Version control like Git is used to coordinate and track changes. The frontend of the web application is developed with Next.js providing a user-friendly interface for interacting with the compression system. This concludes our discussion on the system deployment approach of OptiPict. Stay tuned for more exciting updates on this project. Thank you for listening..

[Audio] Slide 7/12. Let's dive into the implementation of OptiPict. This project uses advanced autoencoder technology to efficiently compress images. The algorithm is trained using a training dataset. For deployment Azure's Web App for Containers service is utilized. Docker Hub is also used to store and provide containers for the application ensuring smooth functionality. And lastly Azure's Web App is seamlessly used for deploying the application. With OptiPict image compression is now more efficient and user-friendly..

[Audio] In conclusion Autoencoders are an invaluable tool for image compression. They have the potential to completely transform fields such as medical imaging video compression autonomous vehicles and social media. Through effectively extracting features from large amounts of data Autoencoders can compress images while retaining essential information. The potential for further advancements is limitless which could lead to even greater compression ratios and higher quality images. This will ultimately result in faster transmission storage and processing of images in critical sectors such as healthcare transportation and media. As we continue to explore the capabilities of Autoencoders it is evident that they will play a pivotal role in shaping the future of image compression..

Future Scope. OptiPict: Autoencoder Based Lossy Image Compression.

[Audio] Slide 10 will cover the revolutionary image compression technology of the OptiPict project which utilizes autoencoders a type of neural network that compresses data such as images by reducing its dimensions. This is achieved by reconstructing the input data with hidden layers resulting in a compressed version of the original image. This approach has shown promising results improving image quality and reducing file size. It has gained significant attention in the field of machine learning with applications continuously expanding. For those interested in delving deeper into autoencoders the slide provides useful references for a gentle introduction and highlights their potential uses in various industries. Image compression has become a crucial aspect of many projects due to the rapidly growing volume of data and the OptiPict project is at the forefront of this technological advancement. This concludes our presentation and we thank you for your attention..

undefined. OptiPict: Autoencoder Based Lossy Image Compression.

[Audio] We have reached the final slide of our presentation. I want to express my gratitude to all of you for joining us. Throughout this presentation we discussed the features and benefits of our project OptiPict. Our utilization of autoencoder technology has revolutionized image compression making it faster more efficient and more accurate. Our project has achieved remarkable results and we are excited to share them with you. We hope this presentation has given you a better understanding of OptiPict and its potential to revolutionize image compression. It has been a pleasure to have you all here. Thank you for your time and attention. Your support means everything to us and we are grateful for your continued interest in our project. And with that we have reached the end of our presentation. Once again thank you for being with us..