Lossless Video Compression Using Bayesian Networks and Entropy Coding

Lossless video compression algorithms are used in applications ranging from archival of video records to the field of medicine. In this paper, we propose a simple yet effective encoding technique for lossless video compression. Our technique automatically learns Bayesian Networks to discover Conditional Dependencies in a video stream through Stochastic Hill Climbing. Utilizing this network, variable length codes are generated using Entropy Coding procedure achieved by Huffman Coding. This structured data is used to encode the video stream. The algorithm has been tested and compared alongside H.264, FF Video Codec 1 (FFV1) and Gzip. The proposed compression model performs on average at least as good and at times better than the aforementioned techniques.