When we talk about compression of video we almost always talk about lossy compressions. When we work with lossy compression it is important to make all changes when authoring the video on the uncompressed data as each cycle of compression will reduce the quality of the video.
Image coherence is the fact that an image is not just noise. It typically shows shapes, it shows gradients. In other words it has some type of data pattern and that can be utilized.
By using these features of the video the algorithms try to code the image in another format that does not use one byte for each pixel in the frame.
Compression algorithms utilizing Frame to frame coherence takes note of the fact that not everything in a movie change from frame to frame. By only storing the pixels that change between frames we can save a lot of information.
Take the same example with 5 frames in sequence with nothing changing, with this idea we store the image once and then information for each frame of what is changed i.e. nothing, we have though reduced the file with a factor of 500%. If the image change we can set a small error value for each pixel when it need saving. We for example only save a new pixel when its value has changed more than 1%. This simple algorithm will produce a lossy compression as we don’t record changes for all the frames where the changes on the pixels were less than 1%, when we decompress the video frames we will not get back the same data as we started with.
Different algorithms are good at different things. For example some compression algorithms will be best depending on what footage you are trying to compress. If there is text you want to read, if there is people not moving much, if there is a lot of camera movement and so on. All will get best result with different types of compression algorithms.
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