Image compression by texture modeling in the wavelet domain

被引:27
|
作者
Ryan, TW
Sanders, LD
Fisher, HD
Iverson, AE
机构
[1] Science Applications International Corporation, Tucson
关键词
D O I
10.1109/83.481668
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
High-quality image compression algorithms are capable of achieving transmission or storage rates of 0.3 to 0.5 b/pixel with low degradation in image quality. In order to obtain even lower bit rates, we relax the usual rms error definition of image quality and allow certain ''less critical'' portions of the image to be transmitted as texture models, These regions are then reconstructed at the receiver with statistical fidelity in the mid-to high-range spatial frequencies and absolute fidelity in the lowpass frequency range. This hybrid spectral texture modeling technique takes place in the discrete wavelet transform domain, In this way, we obtain natural spectral texture models and avoid the boundary blending problems usually associated with polygonal modeling, This paper describes the complete hybrid compression system with emphasis on the texture modeling issues.
引用
收藏
页码:26 / 36
页数:11
相关论文
共 50 条
  • [41] Wavelet-domain edge modeling with applications to image interpolation
    Tao, B
    Orchard, MT
    IMAGE AND VIDEO COMMUNICATIONS AND PROCESSING 2000, 2000, 3974 : 537 - 548
  • [42] Image denoising based on wavelet domain spatial context modeling
    Li, Xuchao
    Zhu, Shan'an
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 344 - 344
  • [43] Multivariate statistical modeling for medical image compression using wavelet transforms
    Wan, Yuehua
    Ji, Shiming
    Yuan, Qiaoling
    Xie, Yi
    PROCEEDINGS OF THE FIFTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, VOLS 1 AND 2, 2006, : 214 - 217
  • [44] Multispectral image compression and encryption method based on tensor decomposition in wavelet domain
    Xu, Dongdong
    Du, Limin
    Du, Yunlong
    High Technology Letters, 2024, 30 (03) : 244 - 251
  • [45] HVS-based image compression scheme in wavelet-contourlet domain
    Gao, Bingkun
    Sha, Baoliang
    Zhang, Yubo
    Bi, Hongbo
    Advances in Information Sciences and Service Sciences, 2012, 4 (02): : 160 - 166
  • [46] WeConvene: Learned Image Compression with Wavelet-Domain Convolution and Entropy Model
    Fu, Haisheng
    Liang, Jie
    Fang, Zhenman
    Han, Jingning
    Liang, Feng
    Zhang, Guohe
    COMPUTER VISION - ECCV 2024, PT L, 2025, 15108 : 37 - 53
  • [47] Multispectral image compression and encryption method based on tensor decomposition in wavelet domain
    徐冬冬
    DU Limin
    DU Yunlong
    HighTechnologyLetters, 2024, 30 (03) : 244 - 251
  • [48] Understanding wavelet image compression
    Mallat, S
    Falzon, F
    WAVELET APPLICATIONS IV, 1997, 3078 : 74 - 93
  • [49] Wavelet transforms and image compression
    Khludov, S. Y.
    Optoelectronics, Instrumentation and Data Processing (English translation of Avtometriya), (02):
  • [50] Wavelet based image compression
    Kaykusuz, ME
    Üner, MK
    PROCEEDINGS OF THE IEEE 12TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, 2004, : 300 - 303