Academic Journal
Space-Frequency Quantization for Image Compression With Directionlets
| Title: | Space-Frequency Quantization for Image Compression With Directionlets |
|---|---|
| Authors: | Vladan Velisavljevic, Baltasar Beferull-Lozano, Martin Vetterli |
| Source: | IEEE Transactions on Image Processing. 16:1761-1773 |
| Publisher Information: | Institute of Electrical and Electronics Engineers (IEEE), 2007. |
| Publication Year: | 2007 |
| Subject Terms: | Video Recording, Numerical Analysis, Computer-Assisted, Signal Processing, Computer-Assisted, 02 engineering and technology, image reconstruction, Data Compression, Image Enhancement, Image Interpretation, Computer-Assisted, Computer Graphics, 0202 electrical engineering, electronic engineering, information engineering, directional transforms, sparse representation, image coding, space-frequency quantization, Algorithms |
| Description: | The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm. |
| Document Type: | Article |
| ISSN: | 1057-7149 |
| DOI: | 10.1109/tip.2007.899183 |
| Access URL: | https://infoscience.epfl.ch/record/134260/files/Space-frequency%20quantization.pdf https://pubmed.ncbi.nlm.nih.gov/17605375 |
| Rights: | IEEE Copyright |
| Accession Number: | edsair.doi.dedup.....eaab4f9e6f49fdd24f35db0fb4828221 |
| Database: | OpenAIRE |
| ISSN: | 10577149 |
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| DOI: | 10.1109/tip.2007.899183 |