Low-bit-rate lossless image coding using classified vector quantization with orthogonal polynomials transform

In this paper, a low bit-rate lossless image coding scheme based on Classified Vector Quantization with Orthogonal Polynomials Transform (OPT) has been proposed. In this work, two vector quantization codebooks VQ1 and VQ2 are used to encode the smooth and edge blocks separately. The proposed scheme divides each input image to be encoded into small blocks and classifies them in spatial domain since it reduces the classification complexity. The smooth blocks are encoded using VQ1 codebook and Adaptive Differential Pulse Code Modulation (ADPCM), so as to provide good response to the non-stationarity of the input data. The edge blocks are vector quantized using VQ2, the OPT is applied on the code vector of edge blocks to obtain transformed coefficient matrix and are encoded with code vector index. The Orthogonal Polynomials Transform maximizes the Energy Packing Efficiency (EPE) which is equivalent to minimizing the Mean Square Error (MSE) in terms of step response. The proposed lossless coding scheme gives better results when compared with existing lossless encoders.

[1]  N. Memon,et al.  A progressive Lossless/Near-Lossless image compression algorithm , 2002, IEEE Signal Processing Letters.

[2]  Guillermo Sapiro,et al.  The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS , 2000, IEEE Trans. Image Process..

[3]  Jia Wen,et al.  An adaptive VQ method used on interferential multi-spectral image lossless compression , 2011 .

[4]  Fouad Khelifi,et al.  An improved SPIHT algorithm for lossless image coding , 2009, Digit. Signal Process..

[5]  Din-Chang Tseng,et al.  Wavelet-based medical image compression with adaptive prediction , 2005, 2005 International Symposium on Intelligent Signal Processing and Communication Systems.

[6]  Moumita Das,et al.  Efficient lossless image compression using a simple adaptive DPCM model , 2001, Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems. MWSCAS 2001 (Cat. No.01CH37257).

[7]  Bhaskar Ramamurthi,et al.  Classified Vector Quantization of Images , 1986, IEEE Trans. Commun..

[8]  Zhuo Wei,et al.  Image Lossless Compression and Secure Transmission System Based on Integer Wavelet Transform , 2010, 2010 Second International Conference on Multimedia and Information Technology.

[9]  Philip Ogunbona,et al.  Hybrid predictive/VQ lossless image coding , 1995 .

[10]  Qiusha Min,et al.  A Hybrid Lossless Compression Scheme for Efficient Delivery of Medical Image Data over the Internet , 2010, 2010 Second International Conference on Computer Modeling and Simulation.

[11]  Shen-Chuan Tai,et al.  Embedded medical image compression using DCT based subband decomposition and modified SPIHT data organization , 2004, Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering.

[12]  SeroussiG.,et al.  The LOCO-I lossless image compression algorithm , 2000 .

[13]  Chu-Sing Yang,et al.  A fast VQ codebook generation algorithm via pattern reduction , 2009, Pattern Recognit. Lett..

[14]  G. Naghdy,et al.  Shape-VQ-based lossless hybrid ADPCM/DCT coder , 2000 .

[15]  Rangaraj M. Rangayyan,et al.  Performance analysis of reversible image compression techniques for high-resolution digital teleradiology , 1992, IEEE Trans. Medical Imaging.

[16]  Xianchuan Yu,et al.  Embedded hybrid coding for lossy to lossless image compression using integer wavelet transform , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[17]  Yiming Zhu,et al.  Lossless Image Compression Based on DPCM-IWPT , 2008, 2008 ISECS International Colloquium on Computing, Communication, Control, and Management.

[18]  Wee Sun Lee Edge-adaptive prediction for lossless image coding , 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096).