Prediction-Based Lossless Image Compression
In this paper, a lossless image compression technique using prediction errors is proposed. To achieve better compression performance, a novel classifier which makes use of wavelet and Fourier descriptor features is employed. Artificial neural network (ANN) is used as a predictor. An optimum ANN configuration is determined for each class of the images. In the second stage, an entropy encoding is performed on the prediction errors which improve the compression performance further. The prediction process is made lossless by making the predicted values as integers both at the compression and decompression stages. The proposed method is tested using three types of datasets, namely CLEF med 2009, COREL1 k and standard benchmarking images. It is found that the proposed method yields good compression ratio values in all these cases and for standard images, the compression ratio values achieved are higher compared to those obtained by the known algorithms.
KeywordsFourier descriptor Wavelet-based countourlet transform Fuzzy c-means classifier Prediction errors Entropy encoding
For this retrospective type of study formal consent is not required.
- 1.Celik M, Tekalp AM, Sharma G (2003) Level-embedded lossless image compression. Acoustics, speech, and signal processing, 2003. In: 2003 IEEE international conference on Proceedings (ICASSP’03), vol 3, pp III–245, IEEEGoogle Scholar
- 3.Ayoobkhan MUA, Chikkannan E, Ramakrishnan K (2017) Lossy image compression based on prediction error and vector quantisation. EURASIP J Image Video Proc 2017 1:35Google Scholar
- 4.Bovik AC (2009) The essential guide to image processing. Academic PressGoogle Scholar
- 5.Nasir DM, Sayood K (1995) Lossless image compression: a comparative study. In: IS&T/SPIE’s symposium on electronic imaging: science and technology. In: International society for optics and photonics, pp 8–20Google Scholar
- 6.Ramakrishnan K, Eswaran C (2007) Lossless compression schemes for ECG signals using neural network predictors. EURASIP J Appl Sig Proc 2007, 1:102–102Google Scholar
- 12.Pandey D, Kumar R (2012) Hybrid algorithm using fuzzy c-means and local binary patterns for image indexing and retrieval. In: Soft computing techniques in vision science. Springer, pp 115–125Google Scholar
- 13.Fabija´nska A (2009) A fuzzy segmentation method for images of heat-emitting objects. In: Iberoamerican congress on pattern recognition. Springer, pp 217–224Google Scholar
- 17.Tommasi T, Caputo B, Welter P, Guld MO, Deserno TM (2009) Overview of the CLEF 2009 medical image annotation track. In: Workshop of the cross-language evaluation forum for european languages. Springer, pp 85–93Google Scholar