EVALUATING THE SHORTCOMINGS OF IMAGE COMPRESSION TECHNIQUES

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1 Global Journal of Computers & Technology Vol. 4, No. 2, April 13, 216 EVALUATING THE SHORTCOMINGS OF IMAGE COMPRESSION TECHNIQUES Sunita Saini, Rohit Mahajan Student of M.TECH. CSE, GCET GURDASPUR Assistant professor, computer science, GCET, GURDASPUR Abstract Image compression has become very imperative mechanism in digital image processing. The main proposal of the compression is to diminish the extent or superfluous data while retaining the in order in the image. The point at the back is to hoard the aptitude of memory mandatory to keep the image(s) or to make use of n etwork bandwidth during wellorganized approach. Transform-based compression is extensively worn for image compression. But transform based methods carry in blocking artifacts within the output image. The compression may also ending in ringing artifacts ar ound edges. The proposed compression technique will integrate SVD-WDR compression with Gradient-based optimization approach for reduction of blocking artifacts in images. The edge restoration method will also be used as a post processing technique to remove the ringing artifacts from the compressed images. The proposed technique will also verified by using the various standard images for compression. The comparison will also be drawn among the proposed and the existing technique based upon the various standard quality metrics of the compression techniques. Indexing terms/keywords image compression, singular value decomposition, discrete wavelet transform Academic Discipline and Sub-Disciplines Computer science engineering, digital image processing SUBJECT CLASSIFICATION Digital image processing TYPE (METHOD/APPROACH) Provide examples of relevant research types, methods, and approaches for this field: E.g., Historical Inquiry; Quasi - Experimental; Literary Analysis; Survey/Interview INTRODUCTION Image compression is a type of compression. As use and reliance on computers continue to cultivate, so does our significance of efficient ways of storing wide range of data. For, example someone with a website or online catalog that uses dozens or perhaps hundreds of images will most likely need to utilize some form of image compression to store those images. This is because the total amount of space required to keep unadulterated images could be prohibitively large in terms of cost. Although we currently exist in a world of rapidly expanding computing and communication capabilities, with the escalation in computer awareness and, particularly, multimedia, the demand for computer systems and their applications to meet up people's needs can also be rising. Since e very bit incurs a cost when being transmitted or stored, any technology which can be introduced into our existing systems that may reduce these costs is essential. When contemplating raw data that could contain over 5% redundancy, it raises the question Why pay for that redundant information? image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. 2. TYPES OF IMAGE COMPRESSION In the case of video, compression causes some information to be lost; some information at a dep th level is considered not needed for an acceptable reproduction of the scene. This sort of compression is named lossy compression. Audio compression on one other hand is not lossy. It is named lossless compression. Lossy and lossless are two main types of image compression. Lossless compression is commonly used for alleged "discrete" data, such as database proceedings, spreadsheets, word-processing files, and yet several kinds of image and video information. Lossy schemes are capable of achieving higher compression. Under normal viewing conditions, no visible loss is perceived (visually lossless). Lossy image data compression is ideal for application to World Wide Web images for quicker transmission across the Internet 213 P a g e e d i t o r g j c g m a i l. c o m

2 Global Journal of Computers & Technology Vol. 4, No. 2, April 13, 216 CLASSIFICATION OF IMAGE COMPRESSION The compression techniques in general fall into two main categories: entropy encoding, quantizer and source encoding. The difference is that entropy encoding and source encoding techniques that consider the nature of data, and the information it represents. On the other hand source encoding is a type of encoding that operates independent on the information that the source data represents. Image compression techniques can further be divided into two other categories, lossy and lossless. Lossless compression addresses the compression techniques designed to reduce coding and inter pixel redundancy. Whereas lossy compression refers to reduction of data used to represent the image by reducing the psycho visual redundancy in the image. These three types of redundancy will be discussed later. One could say that entropy encoding usually (although not necessarily) leads to lossy compression and source encoding leads to lossless compression. It should be noted that the compression techniques need not be applied to the data independently. Different techniques can be combined to form effective compression techniques. As an examples one could mention the compression technique used in JPEG (see section JPEG Standard) images. The demands on the compression technique used de pend on the system. For example a multimedia presentation system might demand both high compression rates and fast decompression, while video telephone system might demand high compression rate A quantizer merely cut the amount of bits considered necessary acutely to accumulate the malformed coefficients by reducing the precision of these values. Since this can be a many-to-one mapping, it is a lossy process and is the key supply of compression within an encoder. IMAGE COMPRESSION TECHNIQUES a) Singular Value Decomposition: SVD is a successful numerical analysis tool used to analyze matrices. The Singular Value Decomposition of image I of size m x n is obtained by the operation: I= USV Where U is column-orthogonal matrix of size m x m, S could be the diagonal matrix with positive or zero elements of size m x n and transpose of n x n orthogonal matrix V. The diagonal entries of matrix S are referred to as the singular values of I. The columns of U matrix are referred to as left singular vector and the columns of the matrix V are referred to as the proper singular vector of I. Thus, each singular value represents the luminance of image layer and the corresponding couple of singular vector represents the geometry of the image layer. In SVD based im age watermarking, several approaches are possible. b) Transform based technique: Transform domain algorithms exploits spatial frequency information contained in the image to achieve compression. Transformation or frequency domain techniques are based on the manipulation of the orthogonal transform of the image rather than the image itself. Transformation domain techniques are suited for processing the image according to the frequency content. The principle behind the frequency domain methods of image enhancement consists of the computing a 2-D discrete unitary transform of the image, for instance the 2-D DFT, manipulating the transform coefficients by an operator M and then performing the inverse transform. The orthogonal transform of the image has two components magnitude and phase. The magnitude consists of the frequency content of the image. The phase is used to restore the image back to the spatial domain. The usual transform domain enables operation on the frequency content of the image, and therefore high frequency content such as edges and other subtle information can easily be enhanced. An image transform can be applied to an image to convert it from one domain to another. It is a mathematical process done in data (usually, digital images or music) that convert it from one field (time, for example) to another (frequency), usually doing Fourier's or Laplace's Transforms. In the new Domain the data could be more easily handled, for lossy compression, de-noising, sharpening, etc. After edited, data is transformed back to its original domain. c) Spatial domain compression method:-spatial domain techniques directly deal with the image pixels. The pixel values are manipulated to achieve desired enhancement. Spatial domain techniques like the logarithmic trans forms, power law transforms, histogram equalization, are based on the direct manipulation of the pixels in the image. Spatial techniques are particularly useful for directly altering the gray level values of individual pixels uniform manner which in many c ases produces undesirable results. It is not possible to selectively enhance edges or other required information effectively. Now we see two techniques of spatial domain techniques.this technique is used for manipulating or changing an image representing an object in space to enhance the image for a given application. These techniques are based on direct manipulation of pixels in an image. it is also used for filtering basics, smoothing filters, sharpening filters, unsharp masking and laplacian techniques. Spatial domain techniques directly deal with pixels of image. The pixel values are altered to get desired enhancement. Spatial domain techniques like the logarithmic transforms, power law transforms, histogram equalization, are based on the direct manipulation of the pixels in the image. Spatial techniques are particularly useful for directly altering the values of individual pixels and hence the overall contrast of the entire image. But they usually enhance the whole image in a uniform manner which in many cases produces undesirable results. It is not possible to selectively enhance edges or other required information effectively. METHODOLOGY We develop an integrated approach using SVD and WDR with non local means. The overall objective is to im prove the results by combining the above approaches. The proposed algorithm is designed and implemented in MATLAB using image processing toolbox. 214 P a g e e d i t o r g j c g m a i l. c o m

3 Global Journal of Computers & Technology Vol. 4, No. 2, April 13, 216 Input Image Apply SVD_WDR based compression Apply Non local means Apply Edge Preserving Smoothing Final Compressed Image Evaluate Parameters End STEPS OF THE ALGORITHM Step 1: First of all input the image. Step 2: Then SVD_WDR compression will be applied on the input image. Step 3: To apply gradient based optimization on Step 4: apply edge preserving smoothing. Step 5: In this step, the image is compressed of step 4. Step 6: Then, the parameters are evaluated. Step 7: End of algorithm. RESULTS AND DISCUSSION The algorithm is applied using various performance indices like Peak signal to noise ratio (PSNR), Mean squared error (MSE), Bit error rate(ber), root mean square error (RMSE),Structural Symmetrical index value (SSIM). As shown in below given figures, we are comparing the results of various images. As results show that our proposed approach results are much better than existing approaches. After the results we are comparing the proposed approach integrated SVD a nd WDR using non local means against the existing techniques like SVD and WDR. 215 P a g e e d i t o r g j c g m a i l. c o m

4 Global Journal of Computers & Technology Vol. 4, No. 2, April 13, 216 INPUT A).INPUT IMAGE B) IMAGE C) SVD WDR The proposed algorithm is tested on various color images. The algorithm is applied using various performance indices like Peak signal to noise ratio (PSNR), Mean squared error (MSE), Bit error rate(ber), root mean square error (RMSE),Structural Symmetrical index value (SSIM). As shown in below given figures, we are comparing the results of various images. As results show that our proposed approach results are much better than existing approaches. After the results we are comparing the proposed approach integrated SVD and WDR using non local means against the existing techniques like SVD and WDR Table 1contains the values of Mean Square Error (MSE) of the proposed and existing algorithm corresponding to the different images. The MSE of the proposed algorithm has less value than existing algorithm. 216 P a g e e d i t o r g j c g m a i l. c o m

5 PSNR MSE Global Journal of Computers & Technology Vol. 4, No. 2, April 13, 216 TABLE 1: MEAN SQUARE ERROR MSE MSE Mean Square Error Table 2 contains the values of Peak Signal Noise Ratio (PSNR) of the proposed and existing a lgorithm corresponding to the different images. The PSNR of the proposed algorithm has more value than existing algorithm. Table 2: Peak signal Noise Ratio PSNR PSNR Peak Signal Noise Ratio P a g e e d i t o r g j c g m a i l. c o m

6 BER RMSE Global Journal of Computers & Technology Vol. 4, No. 2, April 13, 216 Table 3 contains the values of Root Mean Square Error (RMSE) of the proposed and existing algorithm corresponding to the different images. The RMSE of the proposed algorithm has less value than existing algorithm. Table 3: Root Mean Square Error RMSE RMSE Root Mean Square Error 1 5 Table 4 contains the values of Bit Error Ratio (BER) of the proposed and existing algorithm corresponding to the different images. The BER of the proposed algorithm has less value than existing algorithm.. Table 4: Bit Error Ratio BER BER Bit error Rate P a g e e d i t o r g j c g m a i l. c o m

7 SSIM Global Journal of Computers & Technology Vol. 4, No. 2, April 13, 216 Table 5 contains the values of SSIM (Structural similarity index metric) of the proposed and existing algorithm corresponding to the different images. The proposed algorithm has more value than existing algorithm. Table 5: Structural similarity index metric SSIM SSIM Structural Similarity Index Metric The proposed algorithm also tested on gray scale images. CONCLUSION: This paper has clearly shown that no technique is effective for all images. Each has its own benefits and limitations. Thus still it is an open area of research. Therefore much improvement can be done in image compression. In near future we will propose a new method in order to reduce blocking and ringing artifacts in comp ressed images. The proposed compression technique will integrate SVD-WDR compression with Gradient-based optimization approach for reduction of blocking artifacts in images. The edge restoration method will also be used as a post processing technique to re move the ringing artifacts from the compressed images. The proposed technique will also verified by using the various standard images for compression. The comparison will also be drawn among the proposed and the existing technique based upon the various standard quality metrics of the compression techniques REFERENCES 1. Desale, Rajenda Pandit, and Sarita V. Verma. Study and analysis of PCA, DCT & DWT based image fusion techniques. In Signal Processing Image Processing & Pattern Recognition (ICSIPR), 213 International Conference on, pp IEEE, Digital image processing" [2/6/15] 3. "Digital Image" [2/6/15] -guide-digital-citizenship/ 4. "Digital Images", [22/6/15] 5. Donapati, Srinivas analysis and comparison of the compression ratios of the images of different input formats particularly to RGB input format and YUV 444 format. 6. Ernawan, Ferda, Nur Azman Abu, and Nanna Suryana. "TMT quantization table generation based on psychovisual threshold for image compression." InInformation and Communication Technology (ICoICT), 213 International Conference of, pp IEEE, "Digital Images", [22/6/15] 8. Gupta, Krishan, Mukesh Sharma, and Neha Baweja. "Three different KG version for image compression." In Issues and Challenges in Intelligent Computing Techniques (ICICT), 214 International Conference on, pp IEEE, P a g e e d i t o r g j c g m a i l. c o m

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