The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The proposed technique has been applied on a set of standard test images and the results obtained are significantly encourage compared with Joint P

Image compression is an important tool to reduce the bandwidth and storage
requirements of practical image systems. To reduce the increasing demand of storage
space and transmission time compression techniques are the need of the day. Discrete
time wavelet transforms based image codec using Set Partitioning In Hierarchical
Trees (SPIHT) is implemented in this paper. Mean Square Error (MSE), Peak Signal
to Noise Ratio (PSNR) and Maximum Difference (MD) are used to measure the
picture quality of reconstructed image. MSE and PSNR are the most common picture
quality measures. Different kinds of test images are assessed in this work with
different compression ratios. The results show the high efficiency of SPIHT algori

In this paper three techniques for image compression are implemented. The proposed techniques consist of three dimension (3-D) two level discrete wavelet transform (DWT), 3-D two level discrete multi-wavelet transform (DMWT) and 3-D two level hybrid (wavelet-multiwavelet transform) technique. Daubechies and Haar are used in discrete wavelet transform and Critically Sampled preprocessing is used in discrete multi-wavelet transform. The aim is to maintain to increase the compression ratio (CR) with respect to increase the level of the transformation in case of 3-D transformation, so, the compression ratio is measured for each level. To get a good compression, the image data properties, were measured, such as, image entropy (He), percent root-