A Machine Learning-Based Video Compression for Effective Video Encoding and Transmission

Deep Learning (DL) is revolutionizing video processing, as video is progressively key in daily life. Encoding and transmitting video effectively becomes challenging with fast content resolution and data volume. This research presents the most progressive method for Video Compressing (VC), using DL to enhance encoding and transmission efficiency, demonstrating the need for more cutting-edge methods in digital media. This work uses advanced Machine Learning (ML) to reduce video data size without compromising video quality, enhancing its suitability for high-definition streaming and videoconferencing. The algorithm uses Convolutional Neural Network (CNN)+Recurrent Neural Network (RNN) to improve video quality. CNN captures complex spatial details within each video frame, while LSTM relates across time. The proposed VC achieves high video quality rates compared to traditional methods like H.264 and H.265. It adapts in real-time and optimizes video bandwidth usage, making it useful for live streaming services and video conferencing. The VC has been tested extensively, demonstrating significant bit rate reduction while maintaining excellent video quality. It surpasses modern compression methods, making it a flexible solution to the increasing demands for the best video content. This invention in VC is expected to change digital media distribution for good.