This project aims at designing and assessing the functioning of an enhanced protocol for image transmission over loss-prone crowded or wireless networks. The usual approach to transporting images uses TCP. The main motivation to undertake this project starts with stressing on the disadvantages of using TCP. The main drawback of using TCP for image downloads is that its in-order delivery model interferes with interactivity. TCP provides a general reliable, in-order byte- stream abstraction, but which is excessively restrictive for image data. TCP is not suitable for real-time applications as the retransmissions can lead to high delay and cause delay jitter, which significantly degrades the quality. On the other hand , UDP is a connectionless protocol and is not dedicated to end-to-end communications, nor does it check the readiness of the receiver (requiring fewer overheads and taking up less space). Hence, UDP is a much faster, simpler, and efficient protocol for streaming processes, but it does come with a concerning drawback, prone to packet losses. Packet losses can lead to corrupt images, which is something we want to avoid. Therefore this project is a step taken at tackling that very issue. Although it's built on UDP, it also uses a small programmatically written optimizer that can also be considered as a protocol running on top of UDP. Hence, enhancing and optimizing its features according to our objective. This project enables high quality image streaming without exhausting the buffer , therefore giving us a higher chance of receiving images without corrupting them.

Environment Software : Anaconda
IDE: Jupyter Notebook
Language : Python
Libraries:

• Imported socket for socket programming eg: creation of sockets, etc.
• Imported PIL that is PYTHON IMAGE LIBRARY for opening and closing images via path
• Imported tkinter for making the client login, popup and server login GUI.
• Imported sys for accessing system specific functions.
• Imported cv2, the OpenCV library which in our project is used for handling the images such as encoding, decoding it and streaming it through a netwrok.
• Imported numpy for creating multi-dimensional arrays
• Imported struct for packing data into structures.

• There’s Two GUIs for Client and Server Login.
• Once the Client and Server is successfully logged in , the server starts listening and the client accepts the connection respectively.
• The client then asks for the user’s input for an Image through a file dialog
• Once the image is received the client prints out the location path and the image type
• Then the image is compressed and converted into a numpy array using the Open CV Library.
• The numpy array is a three-dimensional array in height, width and channel format.
• The encoded numpy array is then converted into string ready for streaming.
• In the meanwhile, the client also outputs the length of string and the encoded data for demonstration purposes.
• The Length of data/string is sent as a file header to the server from the client before the actual streaming , enabling the server to know the time till when to keep the channel open and the amount of data to be received.
• The server receives the file header reads the length and prepares to receive the data in each stream in a cyclic way.
• The client starts streaming the data in batch of size 1024 bytes (The buffer size) it also at the end of the transmission checks for any remaining data less than 1024 Bytes and if found streams them as well.
• The server prints out each stream it receives with the particular size of data received along with the total amount of data received till then.
• The server also outputs in the end an confirmation message, along with the total streams received , the amount of lost packets, loss percentage and Success percentage.
• The Client and Server Primarily run on UDP but since we have defined how the data should be transferred this can also be considered as a small protocol that sits on top of UDP.
• By Using UDP for streaming rather than TCP/IP as it supports 3 Way handshake which could lead to long idle times while also writing a optimization in the streaming process to stream images in a cyclic way instead of directly exhausting the buffer by trying to transfer it in a single go, Our project has a higher rate of success for image streaming and transferring

Our results satisfy the purpose of our project and shows the successful transmission of images between client and server in an optimized way. There isn’t any data loss or packet loss in the transmissions we performed. Although the project is prone to packet loss as it runs on UDP, because of the optimizer we wrote we decrease that amount significantly. The Image received on the server side had no loss in quality or size. The color values and details of the image was not lost in transmission. We also displayed the number of streams , packet loss, packets received and also the percentages for the same.

Everly Precia Suresh

Vivek Singh Baghel

Kaviyashre Ragupathy