Design and Development of Deep learning based Video Compression for High Efficient Video Scheme

Abstract

Neural networks blended with video processing will be the current topic in newlinediscussion during this era. The impact of deep learning networks and CNNs in newlinevideo compression is a detailed topic to discuss in the current era because the newlineadvancement and efficiency improvement acquired and going to be acquired from newlinethat are expected to be tremendous. The advancement in the area of video newlinecompression becomes a need. Combining the deep learning techniques in newlineprediction, encoding, etc. of video compression helps it act smarter than it is and newlineprovide better advancement and efficiency in compression technique by newlineintelligently handling each steps in compression. The main focus is to merge the newlinedeep learning techniques in various steps of video compression to evolve a smart newlineand efficient deep learning-based compression technique. Advantages of involving newlineneural networks in video processing include the content adaptivity of neural newlinenetworks compared to traditional methods. CNN and deep learning models can use newlineboth near and far pixel details, whereas traditional signal processing can utilise newlineonly neighbouring pixels. Content analysis is an advantage in CNN. Incorporating newlinethe goodness of deep learning to make advancements in the traditional steps if newlinecompression helps in a smart compression method that enhances the efficiency of newlineexisting compression techniques. newlineThe main objective of this research is to integrate deep learning techniques into newlinedifferent stages of video compression, with the aim of creating an intelligent and newlineefficient deep learning-based compression methodology. One of the advantages of newlineusing deep learning in video processing is the ability of neural networks to adapt to newlinecontent variations, surpassing traditional approaches. Unlike conventional signal newlineprocessing, convolutional neural networks (CNN) and deep learning models can newlineleverage both local and global pixel details, benefiting from content analysis. By newlineharnessing the strengths of deep learning, newline