Resolutıon Enhancement Based Image Compression Technique using Singular Value Decomposition and Wavelet Transforms

Abstract

In this chapter, we propose a new lossy image compression technique that uses singular value decomposition (SVD) and wavelet difference reduction (WDR) technique followed by resolution enhancement using discrete wavelet transform (DWT) and stationary wavelet transform (SWT). The input image is decomposed into four different frequency subbands by using DWT. The low-frequency subband is the being compressed by using DWR and in parallel the high-frequency subbands are being compressed by using SVD which reduces the rank by ignoring small singular values. The compression ratio is obtained by dividing the total number of bits required to represent the input image over the total bit numbers obtain by WDR and SVD. Reconstruction is carried out by using inverse of WDR to obtained low-frequency subband and reconstructing the high-frequency subbands by using matrix multiplications. The high-frequency subbands are being enhanced by incorporating the high-frequency subbands obtained by applying SWT on the reconstructed low-frequency subband. The reconstructed low-frequency subband and enhanced high-frequency subbands are being used to generate the reconstructed image by using inverse DWT. The visual and quantitative experimental results of the proposed image compression technique are shown and also compared with those of the WDR with arithmetic coding technique and JPEG2000. From the results of the comparison, the proposed image compression technique outperforms the WDR-AC and JPEG2000 techniques.

Using Wavelets for Gait and Arm Swing Analysis

“…Conventional processing techniques and methods allow to filter signals in a frequency range, extract relevant characteristics such as maximum and minimum peaks, fill data by interpolation, and transform and decompose signals in other domains such as frequency and time. Among these processing techniques, wavelet has shown to have a broad application panorama; the literature documents wavelet uses in different and varied fields such as detection of anomalies associated with seismic events in ultralow-frequency geomagnetic signals [32]; it is also possible to use wavelet techniques for image compression, as detailed in [33], who decompose into singular values and use a discrete wavelet transform to improve the maximum ratio of signal-to-noise ratio compared to techniques such as JPEG2000.…”