# Image Compression by using DWT and DCT Techniques Case Study

Document Type:Research Paper

Subject Area:Computer Science

The specific objective is to determine how DWT and DCT techniques affect the quality of the compressed images. The Discrete Wavelet Transform represents images as a computation of wavelet functions based on different resolution levels. Depending on the choice of wavelet function there exists a large section of wavelet families. The wavelet image representations are tested. The results of the image quality, compression ratios and resolutions are given. While lossless pressure is valuable for correct reproduction, it for the most part does not give adequately high pressure proportions to be really valuable in picture pressure. In the present day, there exist many applications which usea large number of images for solving problems. These digital images can be stored on a disk.

The development of an effectual image protection has become an urgent prerequisite in the media industry due to the illegal exploitation of original digital objects (Al Haj, 2007). An image is a 2-D signal normally processed by the visual system in the humans. Image compression using either Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) techniques are important since there is an increase in a sufficient amount of image, used in satellite data to archive the images, used in multimedia application as a desktop editing and in web data for image transmission (Arora, 2011). The process of compressing the images has provided a platform where the amount of data used to represent an image is significantly reduced. To achieve significant compressed images, three basic data redundancies of the images are removed.

The processes, according to (Raid &Wesam, 2014), involve the following: 1. Interpixel redundancy:Information is unnecessarily replicated in the representations of the correlated pixels in an image and each pixel is dependent on its neighbors. The wavelets have periodic characteristics since they allow simultaneous analysis of both time and frequencies. DWT carry our digital signals decomposition into various subbands which have an excellent frequency resolution. Discrete Wavelet Transform is based on the new JPEG2000 compressed image standard. Discrete Cosine Transform was first introduced in 1974 (Ali, 2017). It has a wide range of applications in computer science and engineering. DCT Technique The Discrete Cosine Transform is commonly used for handling images, mostly used for compression Algorithm for encoding and decoding. DCT is an important technique used to separate the images into spectral bands in relation to the visual quality of the image.

It is used to convert signals into elementary components (Al Haj, 2007). It involves the use of cosine functions. DCT technique involves data conversion into the total series of cosine waves oscillating at different frequencies. These frequencies appear in the upper corner, left of the DCT. The DCT input is represented by an array of integers measuring 8 by 8 each containing a pixel grey scale level. DWT Technique DWT is multi-determination change system, for the most parts utilized for picture and video pressure to accomplish higher pressure proportion. Different change strategies can be utilized as a part of a request to repay the disadvantages of each other. If there should arise an occurrence of 2-D DWT, the information is gone through the arrangement of both low pass and high pass channel in two headings, the two lines and sections.

This technique was based on Threshold Entropy. (Sofia Douda et. al. , 2011) proposed the latest method which was based on theDiscrete Cosine Transform efficient. The low activity blocks were discarded from the main domain pool. In each level, a threshold is chosen and hard thresholding is applied to the detail coefficients. The third method involves the reconstruction of the computed wavelet using approximation coefficients of level N and the modified coefficients of levels from 1 to N (Amina, 2012). Methods using DCT Discrete Cosine Transform method produces images with a better visual fidelity, better PSNR and less blocking of the artifacts. This technique is applied to the whole image and the images partitioned into a range of blocks. The performance of DCT requires computational intricacy.

An experiment conducted by (Amina, 2012)shows that the threshold value of δ40 was a good choice for different compression ratios. Compression ratio is computed by finding the ratio between the size of the original image and the size of the compressed image. The table below shows the experimental results from the Discrete Wavelet Transform (DWT) technique. Threshold values Size of original image Size of compressed image Compression ratio δ =20 47 KB 2. 16 KB 22:38:1 δ =40 47KB 2. 10*10^10 Comparison between the Discrete Cosine Transform and Discrete Wavelet Transform techniques shows that for Discrete Cosine Transform technique, we achieve a compression ratio of 1. 6 and for Discrete Wavelet Transform techniques; we get a compression ratio of 1. 3 (Katharotiya& Patel, 2011). The results also show that the use of Discrete Wavelet Transform technique gives a better image quality than the DCT technique.

The table below shows a comparative PSNR cover image using a 2-D DWT-DCT method and DWT-DCT. (3)It can pack vitality in the lower frequencies for picture information. (4) It can decrease the blocking curio impact and this impact comes about because of the limits between sub-pictures move toward becoming visible (Bhupendra, 2013) Advantages of DWT 1) It is flexible since it is based on cosine and sin functions of different frequencies. 2) It is easy to filter in or filter out data produced by the image in a given non- stationary waveform. 3) DWT technique produces a Time and Frequency information. 4) Discrete Wavelet Transform has an advantage of producing high quality compressed images. These wavelets are suited best to time-limited data. Errors of the compressed image are reduced by the wavelet-based compression techniques.

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