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Self-encoded chimera transform for digital image representation
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AI
DOI：10.1007/s11042-023-17623-9.png)
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Abstract
En 中文
During the last decade, there has been an increased focus on representing images using mathematical expressions. The initial stages towards achieving this process have already been accomplished in the literature. However, several efforts are still required to convert the image into fully mathematical expressions. The majority of the work involved transforming the image into simplified representations using a codebook, which poses a challenge to achieving the main goal. In this work, a new transformation has been introduced to represent an image as small units. This approach enables direct manipulation of image blocks rather than individual pixels. The transformation is denoted as the self-encoded chimera transform (SECT). Being different from traditional transforms such as discrete cosine transform (DCT), and discrete wavelet transform (DWT), the image representation via the SECT remains in the time domain. The SECT can be calculated across multiple layers to enhance the quality of reconstruction. Each layer is represented by three encoded arrays. The encoding arrays are calculated using simple statistics. The SECT is applied to several standard images and achieved better quality in compare to both State-of-the-art and Standard transforms. Further analysis indicates that quality of the reconstruction improves when number of layers of the SECT has increased.
Keywords:
Mathematical expression
Self-encoded
Codebook
Image transform
DCT
DWT
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W