Artificial neural network applications for data compression in video data transfer protocols

Abstract

The article describes the current state of data transfer protocols and methods of image and video compression through the use of artificial neural networks, namely convolutional multilayer networks and deep structured learning. Based on recent publications, a comparative analysis of the performance of classical compression methods and methods based on neural networks was performed. The most effective are those compression methods which are based on decorrelation transforms, namely discrete cosine (JPEG standard) and wavelet (JPEG-2000 standard) transforms. The transform coefficients have a well-understood physical content of spatial frequencies and can be further quantized for a more optimal representation of components that are less important for human perception. The HEVC standard guarantees a more efficient image compression scheme that further takes advantage of the similarity of adjacent blocks and uses interpolation (intracoding). Based on the HEVC standard, the BPG (better portable graphics) format was developed to be used on the Internet as an alternative to JPEG, which is much more efficient than other standards. An overview of the current state of open standards, provided in the article, gives an explanation of what properties of neural networks can be applied to image compression. There are two approaches towards the compression using neural networks: in case of the first approach neural network is used as a part of an existing algorithm (hybrid coding), and in case of the second approach the neural network gives a concise representation of the data (compression network). The final conclusions were made as regards to the application of these algorithms in H.265 protocol (HEVC) and the possibility of creating a new protocol which is completely based on the neural network. Protocols using neural network show better results during image compression, but are currently hard to be subjected to standardization in order to obtain the expected result in case of different network architects. We may expect and predict an increase in the need for video transmission in the future, which will bump into the imitating nature of classical approaches. At the same time, the development of specialized processors for parallel data processing and implementation of neural networks is currently underway. These two factors indicate that neural networks must be embedded into the industrial data standards.