Video compression of coronary angiograms based on discrete wavelet transform with block classification

A new method of video compression for angiographic images has been developed to achieve high compression ratio (~20:1) while eliminating block artifacts which leads to loss of diagnostic accuracy. This method adopts motion picture experts group's (MPEGs) motion compensated prediction to takes advantage of frame to frame correlation. However, in contrast to MPEG, the error images arising from mismatches in the motion estimation are encoded by discrete wavelet transform (DWT) rather than block discrete cosine transform (DCT). Furthermore, the authors developed a classification scheme which label each block in an image as intra, error, or background type and encode it accordingly. This hybrid coding can significantly improve the compression efficiency in certain eases. This method can be generalized for any dynamic image sequences applications sensitive to block artifacts.

[1]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[2]  Miguel P. Eckstein,et al.  Psychophysical evaluation of the effect of JPEG, full-frame discrete cosine transform (DCT) and wavelet image compression on signal detection in medical image noise , 1995, Medical Imaging.

[3]  J. Reiber,et al.  Effect of data compression on quantitative coronary measurements. , 1995, Catheterization and cardiovascular diagnosis.

[4]  J. Whiting Recent technical advances in digital coronary angiography , 1994, Current opinion in cardiology.

[5]  Min-Jen Tsai,et al.  Coronary angiogram video compression , 1994, Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94.

[6]  Ulrik Hindo,et al.  Video coding using wavelet transform, windowed motion compensation, and conditional entropy coding , 1994, Other Conferences.

[7]  M P Eckstein,et al.  Improving detection of coronary morphological features from digital angiograms. Effect of stenosis-stabilized display. , 1994, Circulation.

[8]  Bruce Kuo Ting Ho,et al.  Radiological image compression using wavelet transform with arithmetic coding , 1994, Medical Imaging.

[9]  Bruce Kuo Ting Ho,et al.  Block implementation of arithmetic coding for image compression , 1994, Medical Imaging.

[10]  Mutsumi Ohta,et al.  Hybrid picture coding with wavelet transform and overlapped motion-compensated interframe prediction coding , 1993, IEEE Trans. Signal Process..

[11]  Frederic Dufaux,et al.  Motion-compensated generic coding of video based on a multiresolution data structure , 1993 .

[12]  Bruce Kuo Ting Ho,et al.  Mathematical model to quantify JPEG block artifacts , 1993, Medical Imaging.

[13]  H K Huang,et al.  The Effect of Irreversible Image Compression on Diagnostic Accuracy in Thoracic Imaging , 1993, Investigative radiology.

[14]  H. K. Huang,et al.  Subperiosteal resorption: effect of full-frame image compression of hand radiographs on diagnostic accuracy. , 1992, Radiology.

[15]  Martin Vetterli,et al.  Wavelets and filter banks: theory and design , 1992, IEEE Trans. Signal Process..

[16]  G R Duckwiler,et al.  Fluid Equations Applied to Blood Flow Measurement Using Digital Videodensitometry , 1992, Investigative radiology.

[17]  Michel Barlaud,et al.  Image coding using wavelet transform , 1992, IEEE Trans. Image Process..

[18]  Bruce Kuo Ting Ho,et al.  Specialized module for full frame radiological image compression , 1991 .

[19]  H. K. Huang,et al.  Design and implementation of full-frame, bit-allocation image-compression hardware module. Work in progress. , 1991, Radiology.

[20]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[21]  Ming Lei Liou,et al.  Overview of the p×64 kbit/s video coding standard , 1991, CACM.

[22]  P. Ferrelle Recursive block coding for image data compression , 1990 .

[23]  M. GHANBARI,et al.  The cross-search algorithm for motion estimation [image coding] , 1990, IEEE Trans. Commun..

[24]  Martin Vetterli,et al.  Wavelets and filter banks: relationships and new results , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[25]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  David H. Staelin,et al.  Encoding of images based on a lapped orthogonal transform , 1989, IEEE Trans. Commun..

[27]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[28]  Ian H. Witten,et al.  Arithmetic coding for data compression , 1987, CACM.

[29]  Terry A. Welch,et al.  A Technique for High-Performance Data Compression , 1984, Computer.

[30]  Jae Lim,et al.  Reduction Of Blocking Effects In Image Coding , 1984 .

[31]  Jae S. Lim,et al.  Reduction of blocking effect in image coding , 1983, ICASSP.

[32]  Bruce Kuo Ting Ho,et al.  Applying wavelet transforms with arithmetic coding to radiological image compression , 1995 .

[33]  Ya-Qin Zhang,et al.  Multiscale Video Representation Using Multiresolution Motion Compensation and Wavelet Decomposition , 1993, IEEE J. Sel. Areas Commun..

[34]  Murray H. Loew,et al.  A combined-transform coding (CTC) scheme for medical images , 1992, IEEE Trans. Medical Imaging.

[35]  B. K. Stewart,et al.  Picture archiving and communication systems (PACS) for radiological images: state of the art. , 1988, Critical reviews in diagnostic imaging.

[36]  Hsueh-Ming Hang,et al.  An efficient block-matching algorithm for motion-compensated coding , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[37]  Robert A. Kruger,et al.  Basic concepts of digital subtraction angiography , 1984 .