Simple fast and adaptive lossless image compression algorithm

Abstract

In this paper we present a new lossless image compression algorithm. To achieve the high compression speed we use a linear prediction, modified Golomb-Rice code family, and a very fast prediction error modeling method. We compare the algorithm experimentally with others for medical and natural continuous tone grayscale images of depths of up to 16 bits. Its results are especially good for big images, for natural images of high bit depths, and for noisy images. The average compression speed on Intel Xeon 3.06 GHz CPU is 47 MB/s. For big images the speed is over 60 MB/s, i.e., the algorithm needs less than 50 CPU cycles per byte of image.

An evaluation of lossless compression algorithms for medical infrared images

“…All other algorithms are noticeably slower -from 39% (Lossless JPEG) to 82% (PNG). Our aim in this study was to analyse popular "standard" image compression algorithms; however we note, that there exist other methods with compression speeds over 2 times higher than JPEG-LS [2], [13]. The speeds of JPEG2000, PNG and CALIC are similar (CALIC is faster than the remaining two algorithms by 11% respectively 20%).…”

Performance evaluation of predictors for lossless compression of infrared images with high bit-depth

“…Genellikle entropi kodlayıcısının performansını artırmak için veya üretilen nihai bit uzunluğunu azaltmak için öngörü hata değerlerinin üzerinde ilave bir düzenleme işlemi yapmak önerilmektedir [10]. Bu işlem öngörü hataların modüler aritmetikle yeniden hesaplanması ve bu öngörü hata değerlerinin yeniden düzenlenmesi olarak iki aşamadan oluşmaktadır.…”

“…Ancak modüler aritmetik sonucu oluşan öngörü hata dağılımı Lablacian dağılımına yaklaşmaktadır [10]. Bahse konu dağılım aşağıdaki denklem kullanarak …”

A Novel Real Time Algorithm for Remote Sensing Lossless Data Compression based on Enhanced DPCM

“…Typical grayscale images are of 8 to 16 bit depth [11]. RGB images are consisting of three matrixes of R, G and B each of which has the same depth to grayscale.…”