Resolution in image coding: a comparison between different algorithms

Jean-Bernard Martens

doi:10.1117/12.19679

1 October 1990 Resolution in image coding: a comparison between different algorithms

Jean-Bernard Martens

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.

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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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