MJ2 is a video category category format that contains the motion sequence of JPEG 2000 images. This video encoding system, which uses JPEG 2000 intraframe compression technology, was developed at the Fraunhofer Institute of Integrated Circuits (FIS). Since November 2001, it has existed as an international ISO / IEC standard. The codec is based on a discrete conversion. Compared to the MPEG family used in codecs, it performs encoding in two areas simultaneously. Both in spatial and in frequency. In this case, there is no need to split the images into blocks. Only intra-frame compression is used for each frame, and inter-frame coding is not applied. A distinctive feature of MJ2, that is, Motion JPEG 2000 Video Clip, is that it can be scaled. And not only in terms of frame size. And in quality, that is, in terms of video stream speed, too. The decomposition features are such that each frame can have its own copies, which are two times less vertically and horizontally. Since it is possible to compress without loss, it is possible to use this codec in the medical industry. After all, it is necessary for images to have high accuracy. Synchronization with video supports embedded audio.
JPE is the graphic format of the raster image category. It was developed by the standards committee of the Joint Photographic Experts Group. It is a 24-bit compressed graphics format. Typically, it is used for web resources. In most cases, it is used for photographs or images in which there are many colors. Also well suited for many digital cameras to store digital photos. Images that have this extension use lossy compression. And because the quality of individual photos is getting worse. The greater the compression, the less quality the image becomes. The purpose of JPE files is to compress photorealistic images with small color losses. Provides the ability to achieve high compression ratios. We emphasize that usually the maximum compression of graphic information leads to some loss of information. That is, the compression algorithm changes the original data so that the image that is obtained after restoration will be different from the original image, that is, compressed. This compression method is used to work with full-color images that have high photographic quality. During compression, the Discrete-Cosine Transform (DCT), Huffman’s Code quantization and coding are used.