In order to promote wider use of this new compression method, our fpack and funpack image compression software is available from the HEASARC and NOAO websites. When applied to a large sample of astronomical images, the Rice compression method produces 1.4 times better compression, has 2–3 times faster compression speed, and has about the same uncompression speed as GZIP. Capitalizing on the simplicity of the QOI algorithm, the QOID decoder core can decompress images at a very high speed and with minimal silicon resources. ![]() We compare the widely used GZIP compression program to a newer compression method that uses the Rice algorithm within the FITS tiled-image compression convention. It has a compression efficiency close to that of the PNG compression, at a fraction of the computational complexity. ![]() We use a simple procedure to measure the equivalent number of noise bits in an image, which sets an upper limit on the compression ratio. With SReC frames lossless compression, this project was trained to compress. ![]() This paper compares two image compression methods and shows how they are affected by the amount of noise in the image. Released on Github in 2020, Lossless Image Compression through Super-Resolution project combines neural networks with image compression. A Comparison of Lossless Image Compression Methods and the Effects of NoiseĤ11, Astronomical Data Analysis Software and Systems XVIII
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