Improved Lossless Image Compression Using Adaptive Image Rotation

State-of-the-art compression schemes generally have the ability to adapt themselves to the properties of the data to be compressed. This is a kind of learning process and requires the modification of internal variables influencing the treatment of subsequent data segments. In other words: the order of processing has an impact on the compression performance. In image compression, the order can be changed, for example, by rotating the input image by 90°, 180°, or 270°. In application to lossless screen content compression, investigations with different compression schemes (LOCO-I, HEVC, FP8v3, and SCF) have shown that the rotation has a considerable impact on the compression performance. The difficulty, however, is to predict the best rotation. For the SCF (soft context formation) compression scheme, we have developed a method based on a tiny neural network that suggests a suitable rotation by evaluating basic colour properties of the image to be compressed. The compression can be improved by 0.7098% to 1.3817% depending on the image set tested.