TY - GEN
T1 - Vector quantization of multiresolution morphological pyramids for efficient coding of images
AU - Zhang, Zhiyang
AU - Mitra, Sunanda
PY - 1995
Y1 - 1995
N2 - Morphological pyramid has been proven to be a useful tool in image compression due to low computational complexity, simple implementation and good compression performance based on minimization of entropy. Several morphology based pyramid decomposition techniques already exist. These techniques use morphological filters prior to the down sampling of images. The coding schemes developed commonly omit the first error image of the error pyramid to achieve high compression ratios. However, fine image details may be lost in this process. In order to get high quality lossy image, an estimator involving connectivity preserving filters for the first error image has been used. By using this estimator, the bits per pixel required to code the first error image can be reduced by 30 to 40 percent to obtain `near lossless' compression. In this paper, we apply variable Vector Quantization (VQ) to pyramid coding. We compare the performance of the above estimator to that of VQ scheme. For multi-level pyramid, we discuss accumulative errors and use a modified pyramid generation structure to reduce the accumulative errors. We perform our comparison on two standard images and use Peak Signal to Noise Ratio to judge the compression efficiency and visual quality.
AB - Morphological pyramid has been proven to be a useful tool in image compression due to low computational complexity, simple implementation and good compression performance based on minimization of entropy. Several morphology based pyramid decomposition techniques already exist. These techniques use morphological filters prior to the down sampling of images. The coding schemes developed commonly omit the first error image of the error pyramid to achieve high compression ratios. However, fine image details may be lost in this process. In order to get high quality lossy image, an estimator involving connectivity preserving filters for the first error image has been used. By using this estimator, the bits per pixel required to code the first error image can be reduced by 30 to 40 percent to obtain `near lossless' compression. In this paper, we apply variable Vector Quantization (VQ) to pyramid coding. We compare the performance of the above estimator to that of VQ scheme. For multi-level pyramid, we discuss accumulative errors and use a modified pyramid generation structure to reduce the accumulative errors. We perform our comparison on two standard images and use Peak Signal to Noise Ratio to judge the compression efficiency and visual quality.
UR - http://www.scopus.com/inward/record.url?scp=0029481708&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029481708
SN - 0819419524
SN - 9780819419521
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 723
EP - 733
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Casasent, David P.
T2 - Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
Y2 - 23 October 1995 through 26 October 1995
ER -