Wavelet-based fixed and embedded L-infinite-constrained image coding

被引:13
作者
Alecu, A
Munteanu, A
Schelkens, P
Cornelis, J
Dewitte, S
机构
[1] Free Univ Brussels, Elect & Informat Proc Dept, B-1050 Brussels, Belgium
[2] Royal Meteorol Inst Belgium, B-1180 Brussels, Belgium
关键词
D O I
10.1117/1.1581731
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A new wavelet-based L-infinity-constrained fixed and embedded coding technique is proposed. The embedded bit stream can be truncated for any desired distortion bound at a corresponding bit rate, so that the target upper bound on the elements of the reconstruction error signal is guaranteed. The original image can also be coded up to a fixed a priori user-defined distortion bound, ranging up to lossless coding. A lifting-based wavelet decorrelating transform is employed on the original image, and exact relations are established between spatial and wavelet domain distortions. The wavelet coefficients are quantized by symmetric uniform quantizers for fixed-distortion coding and by families of embedded uniform deadzone scalar quantizers for embedded coding. The quantized coefficients are finally losslessly encoded using a quadtree-based coding algorithm. Any floating-point lifting-based wavelet transform can be used, and a few of the popular wavelet transforms included in the JPEG2000 verification model are worked out as examples. We compare other L-infinity-constrained coding schemes and show that our proposed coder offers in addition a fully embedded L-infinity-oriented bit stream. We illustrate also that the proposed coder retains the same capabilities as the state-of-the-art embedded wavelet-based co-decs, while providing superior compression results and embeddedness with respect to the L-infinity distortion measure. (C) 2003 SPIE and IST.
引用
收藏
页码:522 / 538
页数:17
相关论文
共 20 条
[1]   MAXAD distortion minimization for wavelet compression of remote sensing data [J].
Alecu, A ;
Munteanu, A ;
Schelkens, P ;
Cornelis, J ;
Dewitte, S .
MATHEMATICS OF DATA/IMAGE CODING, COMPRESSION, AND ENCRYPTION IV, WITH APPLICATIONS, 2001, 4475 :149-160
[2]  
ALECU A, 2000, P SOC PHOTO-OPT INS, V4170, P182
[3]   Near-lossless image compression techniques [J].
Ansari, R ;
Memon, N ;
Ceran, E .
JOURNAL OF ELECTRONIC IMAGING, 1998, 7 (03) :486-494
[4]   NEAR-LOSSLESS COMPRESSION OF MEDICAL IMAGES THROUGH ENTROPY-CODED DPCM [J].
CHEN, KS ;
RAMABADRAN, TV .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1994, 13 (03) :538-548
[5]  
CHRISTOPOULOS C, 2000, WG1N1878 ISOIEC JTC
[6]  
DAUBECHIES I, 1996, FACTORING WAVELET TR
[7]  
Gersho A., 1992, VECTOR QUANTIZATION
[8]   Image coding with an L∞ norm and confidence interval criteria [J].
Karray, L ;
Duhamel, P ;
Rioul, O .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 1998, 7 (05) :621-631
[9]   Near-lossless image compression: Minimum-entropy, constrained-error DPCM [J].
Ke, LG ;
Marcellin, MW .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 1998, 7 (02) :225-228
[10]   Vector quantization using the L(omega) distortion measure [J].
Mathews, VJ ;
Hahn, PJ .
IEEE SIGNAL PROCESSING LETTERS, 1997, 4 (02) :33-35