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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-3, 2017 ISSN: 2454-1362, http://www.onlinejournal.in

Analysis of Compression using DCT

Pranavi Patil1, Sanskruti Patil2 & Harshala Shelke3 & Prof. Anand Sankhe4 1,2,3 Bachelor of Engineering in , Mumbai University 4Proffessor, Department of Computer Science & Engineering, Mumbai University Maharashtra, India

Abstract: Video is most useful to represent the where in order to get the best possible compression great information. , are the most efficiency, it considers certain level of . essential approaches to represent data. Now this year, all the communications are done on such ii. : media. The central problem for the media is its large The lossless compression technique is generally size. Also this large data contains a lot of redundant focused on the decreasing the compressed output information. The huge usage of digital video' without any alteration of the frame. leads to inoperable growth of data flow through The decompressed bit-stream is matching to the various mediums. Now a days, to solve the problem original bit-stream. of bandwidth requirement in communication, multimedia data is a big challenge. The main 2. Video Compression concept in the present paper is about the Discrete The main unit behind the video compression Cosine Transform (DCT) algorithm for compressing approach is the video encoder found at the video's size. Keywords: Compression, DCT, PSNR, side, which encodes the video that is to be MSE, Redundancy. transmitted in form of bits and also the positioned at the receiver side, which rebuild the Keywords: Compression, DCT, PSNR, MSE, video in its original form based on the bit sequence Redundancy. given at the encoder. Video compression reduces redundancy and irrelevancy. 1. Introduction Video is an effective manner of entertainment Sources of Redundancy: and communication, now a day. But it needs a vast 1) Spatial storage and transmission bandwidth. For example, A 2) Temporal video of 90 minutes, of 30 frames per second and 750*570 resolution, it will needs 2.78 Irrelevancy: GB storage. The storage and bandwidth need for this Intra coded frame (I) is very high, compressed size of Inter coded frame (P) such video is upto a certain MB's, then requirement Bi-directional frame (B) of storage can be reduced to a large size. Video compression provides the way to compress the video's size. The videos and images contain a big amount of redundant information. The main procedure in the video compression is about rejecting this redundant information, which is unobservable for the human eyes. In the procedure of video compression, algorithm is put onto the input uncompressed video to generate the output compressed video, which can be efficient for transmission or storage. Video compression decreases the file size, so that Fig.1 MPEG Encoding pattern of video frames compressed video needs an realistic amount of download time. Following are the two types of The video combines the large number of compression methods are: frames. Initially, the video is transformed into the sequence of frames which are nothing but images i. : sequence. The intra coding method used for The investigation of the study in compression of I frame. compression is conquered by lossy compression,

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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-3, 2017 ISSN: 2454-1362, http://www.onlinejournal.in

Video Compression Techniques: There is several compression techniques developed for compressing the video efficiently. The mainly used techniques are the Discrete Cosine Transform (DCT) compression and Discrete (DWT) compression. In video compression, the video is converted into individual frames and then various techniques of compression are applied on that each frame. The compressed frames are decompressed and again a video could be created from those, so that the output of compressed video is obtained. Also the DCT method, initially converts the video into the various number of frames. Each frame is again divided into the small blocks and then the DCT algorithm is applied to each frame. After applying the DCT algorithm, it converts every value into the frequency domain. This conversion process happened in such a way that the low Fig.2 Video compression system using DCT algorithm frequencies are on the top-left and higher frequencies are on the bottom right. Then the quantization is Video compression system using DCT done, because of quantization the DCT coefficients algorithm To compress the video, initially the video become integers as they have been scaled by a needs to be converted into individual frames and then scaling factor. By applying Inverse Discrete Cosine compression techniques are applied on each frame. Transform (IDCT) algorithm, original input frames To compress the picture, DCT applies to each frame can be reformed. and thus, type of the DCT method is an intra-frame In Discrete wavelet transform (DWT), the compression. Once, compression of all the frames compression techniques directly applied on the frame are done, the sequence of compressed frame forms as a whole (i.e., frame need not be divided into the compressed video, whose size is relatively smaller blocks). The main intention of this smaller than original video. compression algorithms is to store up the frame data Basically in the DCT method, the compression as in small space as possible. occurs in a three step process. Firstly, each frame is divided into small blocks and then DCT is applied on 3. Proposed System each frame, after applying DCT on each frame, it In the several communication systems, the converts the entire pixel values into frequency problem with video is its large size only, So that size domain such that the larger frequency reside compression of video is required to save storage into the bottom-right and the smaller frequency space. The compression techniques mainly searches pixels reside into the top-left places in the matrix. As for the redundancies among the several frames and the eye of human is sensible only to lower frequency also the correlation among the several frames to pixels and thus, the higher frequency pixels are obtain compression of high degree. In the proposed rejected. Secondly, the quantization is applied onto method, the up-down sampling based approach is the obtained matrix such that, the coefficients reject used. Here the system is presenting a Discrete Cosine their values after the decimal point. Depending on Transform (DCT) algorithm to perform the video this, the scaling factor is choosed, so that even after compression with scalability factor. the values later than the decimal point are rejected, the value remains almost the similar. Then, finally the coding technique for compression is applied, after that because of applying the Inverse DCT, the original file can be reconstructed. The algorithm used in these technique takes any number of jpeg video frames and the any size video can be compressed. The DCT algorithm is simplest and balanced, so that, it is quite easy to be implemented when compared to the other compression techniques.

4. Error metrics Compressed video output calculated based on PSNR & Compression Ratio. The Mean Square Error

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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-3, 2017 ISSN: 2454-1362, http://www.onlinejournal.in

(MSE) and the Peak to Ratio (PSNR) are two error metrics available to measure the [4] Chowdhury, . Mozammel Hoque, and Amina compression quality of the frame . To compute Khatun. " Using Discrete Wavelet the PSNR, first needs to calculate the mean-square Transform." International Journal of Computer Science (2012). error with the help of following equation:

MSE [5] Jasmeet kaur1 Ms. Rohini Sharma2, “ A Combined DWT-DCT approach to perform Video compression base Compression ratio is a measure of the reduction of of Frame Redundancy” , September 2012. detailed coefficient of data. [6] M.A. El-dosuky1 and Wesam Ahmed “Jpeg Image Compression Using Discrete Cosine Transform – A Survey” A.M.Raid1, W.M.Khedr2, April 2014. PSNR block help to calculate the peak signal- to-noise ratio, in decibels, between two frames. This [7] Saraswathy, K., D. Vaithiyanathan, and R. ratio is frequently used to measure the quality among Seshasayanan. "A DCT approximation with Low the original that is uncompressed and a compressed Complexity for image compression." Communications and frames. Signal Processing (ICCSP), 2013 International Conference on. IEEE, 2013. PSNR = ) [8] G.Suresh,“A Low Complex Scalable Spatial If the PSNR is higher, then the compression quality Adjacency ACC-DCT Based Video Compression or reconstruction of the frame is also better. Method”, 2010 Second International conference on Computing, Communication and Networking 5. Conclusion Technologiespp. 4244, 2010.

In this paper, we have presented a video [9] K.Saraswathy et al. “A DCT Approximation with Low compression algorithm named as Discrete Cosine Complexity For Image Compression”, (2013). Transform (DCT). The input uncompressed video is transformed into frames and the size of the frames is [10] Gupta, Maneesha, and Amit Kumar Garg. "Analysis converted as per the requirement, then the DCT Of Image Compression Algorithm Using DCT." algorithm is applied to each frame. After that the (2012). sequence of compressed frames are obtained and from that compressed frames we form the compressed video. The proposed system gives the more compression ratio and also improves quality of the compressed video as compared to all other algorithms. It gives a high Compression ratio of video as well as gives a better reconstructed quality. These framework could be further enhanced by generating the better video encoders that enable higher quality video streams, higher frame rates and higher resolutions at preserved bit rates (compared with previous standards), or the similar quality of the video at lower bit rates.

References

[1] Navpreet Saroya*, Prabhpreet Kaur “Analysis of IMAGE COMPRESSION Algorithm Using DCT and DWT Transforms.” Department of Computer Science and Engineering GNDU, Amritsar, Punjab, India, February 2014.

[2] Telagarapu, Prabhakar, et al. "Image Compression Using DCT and Wavelet Transformations." International Journal of Signal Processing, Image Processing and Pattern Recognition 4.3 (2011).

[3] Elamaran, V., and A. Praveen. Comparison of DCT and wavelets in image coding." Computer Communication and Informatics (ICCCI), 2012 International Conference on. IEEE, 2012.

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