Fuzzy-based video compression using bilinear fuzzy relation equations

We present a novel color video compression method using the greatest solution of a system of bilinear fuzzy relation equa tions to assess the similarity between frames. The frames in each band are treated separately and each frame is classifed as an Intra frame or a Predictive frame. A frame is labelled as Predictive frame, and compressed more than an Intra-frame, if the similarity value with the previous Intra frame is higher than a selected threshold; A pre-processing activity is performed to select the optimal threshold value of the similarity between frames. The proposed method allows to supply a high quality of the reconstructed frames and has the advantage of not requiring high CPU time and memory storage for its execution; it was tested on color videos of the Fast-Moving Objects dataset; the results show that it produces better performances than the Lukasiewicz similarity-based video compression method and comparable with those achieved by MPEG-4 and the deep learning video compression method DVC_pro. The results show that the quality of the reconstructed frames obtained with BFRE is comparable with that of DVC Pro, but has a lower computational complexity, providing better performances in terms of video encoding speed.