Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: II. Results and analysis

Vector quantization (VQ) is a well-established signal and image compression transform that exhibits several drawbacks. First, the VQ codebook generation process tends to be computationally costly, and can be prohibitive for high- fidelity compression in adaptive real-time applications. Second, codebook search complexity varies as a function of image statistics, codebook formation technique, and prespecified matching error. For large codebooks, search overhead can be prohibitive for VQ compression having stringent constraints on matching error. A third disadvantage of VQ is codebook size, which can be reduced at the cost of fidelity of reproduction in the decompressed image. Such issues were discussed in Part 1 of this series of two papers.

[1]  Kuldip K. Paliwal,et al.  Fast nearest-neighbor search based on Voronoi projections and its application to vector quantization encoding , 1999, IEEE Trans. Speech Audio Process..

[2]  O. Tsen,et al.  Evaluation of edge detection algorithms for nuclear medicine images via ROC and shape analysis with adaptive thresholding , 1981, IEEE Transactions on Nuclear Science.

[3]  Yi-Shao Lai,et al.  An Observation on Fractal Characteristics of Individual River Plan Forms , 1993, Fractals in the Natural and Applied Sciences.

[4]  Gerhard X. Ritter,et al.  Image quality measures for performance assessment of compresssion transforms , 1998, Optics & Photonics.

[5]  Mark S. Schmalz.  Detection of small targets in compressed imagery: II. Performance of advanced edge detectors, morphological operations, and component labeling over VQ- and VPIC-compressed imagery , 1997, Defense, Security, and Sensing.

[6]  Frank M. Caimi,et al.  Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory , 1999, Optics + Photonics.

[7]  G. Poggi.  Fast algorithm for full-search VQ encoding , 1993 .

[8]  Yong Xu,et al.  Tree-structured vector quantization design using adaptive genetic algorithms , 1998, Other Conferences.

[9]  Andrew G. Tescher,et al.  Practical issues for transform coding of multispectral imagery , 1995, Remote Sensing.

[10]  Henry Leung,et al.  Use of IFS for track fusion , 1996, Defense, Security, and Sensing.

[11]  Sergio S. Bosso.  Applications of lithium niobate integrated optic in telecommunication systems , 1999, Photonics West.

[12]  Mahmoud R. El-Sakka,et al.  Adaptive image compression based on segmentation and block classification , 1999 .

[13]  Mohamed S. Kamel,et al.  Adaptive image compression based on segmentation and block classification , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[14]  Mohamed S. Kamel,et al.  Adaptive image compression based on segmentation and block classification , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[15]  Gerhard X. Ritter,et al.  Performance analysis of compression algorithms for noisy multispectral underwater images of small targets , 1997, Defense, Security, and Sensing.

[16]  Alan C. Bovik,et al.  Visual pattern image coding , 1990, IEEE Trans. Commun..

[17]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[18]  Mark S. Schmalz.  Detection of small targets in compressed imagery: I. Performance of edge detectors over VQ- and VPIC-compressed imagery , 1997, Defense, Security, and Sensing.