JOURNAL ARTICLE

Still Image Compression using Embedded Zerotree Wavelet Encoding

Vishal ShingateDr.T.R. SontakkeProf. Dr. S.N. Talbar

Year: 2010 Journal:   International Journal of Computer Applications Vol: 1 (7)Pages: 65-75

Abstract

This paper proposes a Embedded Zerotree Wavelet Encoding (EZW) for image compression. Shapiros Embedded Zerotree Wavelet encoder or EZW encoder for short (Sha93). An EZW encoder is an encoder specially designed to use with wavelet transforms, which explains why it has the word wavelet in its name. The EZW encoder was originally designed to operate on images (2D-signals) but it can also be used on other dimensional signals. The EZW encoder is based on progressive encoding to compress an image into a bit stream with increasing accuracy. This means that when more bits are added to the stream, the decoded image will contain more detail, a property similar to JPEG encoded images. It is also similar to the representation of a number like P. Every digit we add increases the accuracy of the number, but we can stop at any accuracy we like. Progressive encoding is also known as embedded encoding. Coding an image using the EZW scheme, together with some optimizations results in a remarkably effective image compressor with the property that the compressed data stream can have any bit rate desired. Any bit rate is only possible if there is information loss somewhere so that the compressor is lossy. However, lossless compression is also possible with an EZW encoder, but of course with less spectacular results.

Keywords:
Computer science Encoding (memory) Wavelet Image compression Compression (physics) Image (mathematics) Artificial intelligence Data compression Computer vision Image processing Materials science

Metrics

16
Cited By
2.24
FWCI (Field Weighted Citation Impact)
3
Refs
0.89
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Data Compression Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Digital Filter Design and Implementation
Physical Sciences →  Computer Science →  Signal Processing

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