We present a novel color video compression method using the greatest solution of a system of bilinear fuzzy relation equations to assess the similarity between frames. The frames in each band are treated separately and each frame is classified 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.

我们提出了一种新颖的彩色视频压缩方法，使用双线性模糊关系方程组的最大解来评估帧之间的相似性。每个频带中的帧被单独处理，并且每个帧被分类为帧内帧或预测帧。如果与前一帧内帧的相似度值高于选定的阈值，则将帧标记为预测帧，并且比帧内帧压缩更多；执行预处理活动以选择帧之间相似度的最佳阈值。所提出的方法允许提供高质量的重建帧，并且具有不需要高CPU时间和内存存储来执行的优点；它在快速移动物体数据集的彩色视频上进行了测试；结果表明，该方法比 Lukasiewicz 基于相似性的视频压缩方法具有更好的性能，并且与 MPEG-4 和深度学习视频压缩方法 DVC_pro 的性能相当。结果表明，BFRE 获得的重建帧质量与 DVC Pro 相当，但计算复杂度较低，在视频编码速度方面提供了更好的性能。