Innovative Insights: A Review of Deep Learning Methods for Enhanced Video Compression

Abstract

Video Compression (VC) is a significant aspect of multimedia technology, in which the goal to minimize the size of video data, while also preserving its perceptual quality, for effective transmission and storage. Traditional approaches such as transform coding, predictive coding, and entropy coding are some of the much earlier discovered approaches in this area. VC is a challenging concept which plays a significant role in the effective transmission of data with low storage and minimum bandwidth requirements. However, the limited processing power, storage, memory, lower compression rate and lower resolution are some factors that impact the functionality and performance of VC. This survey aims to encompass a comprehensive review of present DL approaches for VC, especially the application of advanced DL-based Neural Network (NN) algorithms that are developed for solving the aforementioned challenges of VC. Adaptability of DL algorithms is exploited to enhance the potential quality of compressed videos and to positively influence lossless video compression outcomes. The DL approaches include Deep Neural Networks (DNN) methods such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Recurrent Neural Networks (RNN), Deep Recurrent Auto-Encoder (DRAE), etc. This survey examines the relationships, strengths as well as problem statements of DL-based compression approaches of VC. Furthermore, this survey also deliberates on datasets, hardware specifications, comparative analysis, and research directions. This survey embeds DL-based computer vision approaches, with hardware accelerators like GPU and FPGA, to minimize the complexity of in a model. This survey aims to overcome the limitations of VC, such as the varying effectiveness of specific encoder approaches, the challenges in utilizing hardware accelerators, low-resource devices, and difficulties in managing the large-scale databases. Integrating DL-based approaches with existing standard codecs remains a significant challenge. Ensuring compatibility, interoperability, and standardization is important for widespread adoption and integration. Enhancing the interpretability and control of DL approaches permit for better customization of compression settings, allowing the users to balance bit rate and quality according to their specific requirements. To gather relevant studies, widespread VC datasets are researched and utilized such as, Ultra-Video-Group dataset (UVG), Video Trace Library (VTL), etc. The selection criteria for this study of VC techniques and deep learning (DL) approaches are chosen to focus on the integration of DL with codecs, which is a primary research area of interest. This integration provides valuable insights into advanced DL applications in overcoming challenges associated with VC. Frameworks such as TensorFlow, Keras, PyTorch are utilized to classify the approaches according to their fundamental NN architectures.

Keywords

• Convolutional neural network
• deep learning
• deep neural network
• generative adversarial network and video compression