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 sub‐
band 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 ignor‐
ing 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 sub‐
bands 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-fre‐
quency subband and enhanced high-frequency subbands are being used to gener‐
ate 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.