Image compression is a very important issue for

Image compression is a very important issue for several applications in the area of multimedia communications, the objective being reduction of storage and transmission costs. Many efficient coding techniques have been developed for various applications. Amongst them, the Joint Photographic Experts Group (JPEG) has been recommended for compression of continuous tone still images. However, the reconstructed images from JPEG compression produce annoying blocking artifacts near block boundaries, particularly in highly compressed images, because each block is transformed and quantized independently. Several techniques/algorithms have been proposed by researchers, both in spatial and frequency domains, for reduction of these artifacts with varied degree of success. These are briefly overviewed here. A new technique working in frequency domain, is proposed here by authors. This paper puts forth a method and an algorithm, working in frequency domain, for the detection and reduction of such blocking artifacts. These artifacts are modeled here as 2-D step functions between two neighboring blocks. Presence of the blocking artifacts is detected by using block activity based on human visual system (HVS) and block statistics. The boundary regions between blocks are identified as either smooth or non-smooth regions. The blocking artifacts in smooth regions are removed by modifying a few DCT coefficients appropriately, whilst an edge-preserving smoothing filter is applied to the non-smooth regions, i.e., genuine edges. The algorithm has been applied to variety of JPEG compressed images and results are compared with other postprocessing algorithms. The reduction in the blocking artifacts for each image have been evaluated using three indices, namely peak signal-to-noise ratio (PSNR), mean structure similarity (MSSIM) index based on human visual perception, and a new index, called here block boundary measure (BBM), applied to both vertical and horizontal block boundaries. The results show that the proposed method is very effective in detecting and reducing the blocking artifacts in JPEG compressed images.INTRODUCTIONThe image compression has turned into very significant instrument in digital image processing. The chief goal of compression is to decrease the quantity or undesirable data whereas holding the information in the picture. As a result, the decompressed image and video exhibit various kinds of distortion artifacts such as blocking, blurring and ringing. Blocking artifact measurement algorithms have an important role to play in the design of image and video coding systems. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. We propose a new approach that can blindly measure blocking artifacts in images without reference to the originals. The key idea is to model the blocky image as a non-blocky image interfered with a pure blocky signal. The task of the blocking effect measurement algorithm is then to detect and evaluate the power of the blocky signal. The proposed approach has the flexibility to integrate human visual system features such as the luminance and the texture masking effects.Inspite of easy implementation and energy compaction property, DCT based transform coding technique produces visible annoying artifact at block boundaries in the reconstructed picture. During recovering original image from compressed image occurs various problems like blocking artifacts, ringing artifacts, blur artifacts or edge artifacts are observed. However quantification of these artifacts is a different task. In this paper discuss various conventional filters, which is used to reduce artifacts in compressed images. The experimental results are illustrating the performance of conventional filters on the basis of MSE and PSNR.                    Fig 1.1. Block Diagram                                          Fig 1.2. Flow DiagramMETHODOLOGYIn the method proposed by Kuo and Hsieh 7, first blocks with visible blocking artifacts are detected then an edge map for these blocks is created and finally edge-sensitive filtering is performed by low pass filtering of the pixels only on one side of the edge without including the pixels on the edge. In the method proposed by Kim et al. 8, the filtering method has two modes, filtering for smooth regions and filtering for other regions. Mode decision is taken for each row of a vertical block boundary or each column of a horizontal block boundary by ratifying the flatness of this row or column by respectively. Filtering for smooth region is performed by using a nine tap one dimensional low pass filter. Filtering for other regions is performed by modifying only the pixels adjacent to the block boundary. The modification is based on a row-wise 4 point DCT analysis on the pixels across the vertical block boundary. Tai et al.9 introduced a novel algorithm for reducing the blocking artifacts. The proposed algorithm is based on 1-d filtering of block boundaries. The masking effect of HVS is considered to improve visual quality. This work shows that using the three filtering modes promotes effective deblocking. The filtering in smooth regions is strong to remove noticeable blocking artifacts. An intermediate filtering mode is proposed to balance strong filtering in smooth regions and weak filtering in complex regions. This weak filter is applied in complex regions. Meier 10 proposed a method to remove blocking artifacts by first segmenting the degraded image in to regions by an MRF segmentation algorithm and then each region is enhanced separately using an MRF model. Vo et al. 11 design an adaptive fuzzy filter to fast remove blocking and ringing artifacts with consideration of edge and texture direction. Chatterjee and Milanfar 12 design a patch based wiener filter that exploits patch redundancy for image denoising. Buades and Coll 13 proposed a new algorithm the non local means (NL-means), based on a non local averaging of all pixels in the image. This filter can be applied to the image block and modify its pixels as the weighted sum of its neighborhood pixels, whose weighted parameters is determined by similarity of image block neighbourhood.           Fig 2.1. Standard Image (256×256)Image Coding-Coding and compression are essential processing tasks in image transmission and storage. Though K-L transform is well known as an optimal scheme for data compression, it has not found widespread applications because of difficulties associated with the computation of eigenvectors of the image covariance matrix. A NN is supposed to become able to exploit structural correlation due to the non-linear processing of the input signal. However a single NN does not provide a significant result specifically from perceptual quality point of view of the image. The reason seems to be the dominance of first order correlation on the edge blocks and most of the images have large number of stationary blocks having linear correlation as compared to edge blocks. Another reason is the substantial variations in the structural correlation between the neighboring blocks along with the restriction on the number of hidden layer neurons, hampers the ability of the NN to learn the training pattern adequately. An effective solution to this is presented in this paper. The proposed method uses multi stage PCE, using NN. Network learning is achieved through RLS algorithm. Coefficients are then coded using zero trees for a large number of insignificant coefficients. Transform Coding-First, we establish the concept of an equivalent scalar (wavelet) filter bank system in which we present an equivalent and sufficient representation of a multiwavelet system of multiplicity r in terms of a set of r equivalent scalar filter banks. This relationship motivates a new measure called the good multifilter properties (GMPs), which define the desirable filter characteristics of the equivalent scalar filters. We then relate the notion of GMPs directly to the matrix filters as necessary eigenvector properties for the refinement masks of a given multiwavelet system. Second, we propose a generalized, efficient, and nonredundant framework for multiwavelet initialization by designing appropriate preanalysis and post-synthesis multirate filtering techniques. Finally, our simulations verified that both orthogonal and biorthogonal multiwavelets that possess GMPs and employ the proposed initialization technique can perform better than the popular scalar wavelets such as Daubechies’D8 wavelet and the D(9/7) wavelet, and some of these multiwavelets achieved this with lower computational complexity.