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1 October 1990 Resolution in image coding: a comparison between different algorithms
Jean-Bernard Martens
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Proceedings Volume 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications; (1990) https://doi.org/10.1117/12.19679
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States
Abstract
A common property of many contemporary image coding algorithms is that they code an image at a number of resolutions. The different algorithms provide alternative solutions to how a low-resolution image should be updated to a high-resolution image with a minimum of additional information. In most available image coding techniques such as subband and transformation coding, both the low-resolution image and the high-resolution information are derived by filtering and subsampling the original image. The available coding algorithms differ mostly in how they accomplish this splitting of the original image into different components, since similar quantization techniques are used in all cases to reduce the data rate of these components. In this paper we present an alternative technique for coding the high-resolution components. We argue that a low-resolution image deviates from the original image because it has to satisfy additional local (symmetry) constraints. In general, all high-resolution components are required to restore the local asymmetry of the original image. However, if the image is neither completely symmetrical nor asymmetrical, as is often the case, then fewer components may be sufficient to restore the original image. We find that the performance of a coding algorithm is mainly determined by how often the local symmetry constraints fail and high-resolution information must be added. In the majority of the cases, one high-resolution coefficient is sufficient to restore the original image.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Jean-Bernard Martens "Resolution in image coding: a comparison between different algorithms", Proc. SPIE 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications, (1 October 1990); https://doi.org/10.1117/12.19679
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KEYWORDS
Image compression
Image filtering
Linear filtering
Quantization
Transform theory
Electronic imaging
Human vision and color perception
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