Methods to reduce the iterations needed to process a given video stream are referred to as video compression techniques. Videos consume a large amount of storage space on computational systems or handheld devices, and video compression is also used to lower the dimensionality of data owing to constraints on storage resources. In this study, we propose a macro block-based fuzzy logic video compression (MB-FL) algorithm. The proposed approach uses a fuzzy-based search to maintain pixel resolution, which is ideal for real time streaming media and thus increases peak signal-to-noise ratio (PSNR) and subjective quality. Compression is performed on repeated frames of data files via complex equations, and the repeating patterns are then substituted with smaller data or coding fragments. Owing bandwidth limitations, compression is often necessary to transmit and receive content over network connections. Using a fuzzy membership function, the multiscale aspect of our method evaluates the connection of individual components in the current frame to those in the reference frame. The results of an experimental evaluation show that the proposed approach significantly compressed files using a fuzzy-based search. We compared the performance of MB-FL with that of existing models to measure the quality of compressed video stream.

Keywords: Computational macro block based fuzzy logic video compression (MB-FL), Membership function (MF), Sum of absolute differences (SAD)

No potential conflict of interest relevant to this article was reported.