Comparison of Various Compression Algorithms

This segment depicts the correlation of different algorithms referenced in the literature study. The fundamental correlation parameters utilized for looking at the lossless satellite image compression calculations are bits per pixel (BPP), compression proportion. blunder flexibility, and multifaceted nature. On account of the lossy algorithm, top sign to commotion proportion and mean square mistake are considered as examination parameters notwithstanding compression proportion and multifaceted nature. Rate contortion bends are utilized to evaluate how much compression can be accomplished by the given lossy algorithm at a specific piece rate. PSNR and mean square error (MSE) values are utilized as bending measures on account of the lossy image compression algorithm. Rate twisting bends are utilized to upgrade the lossy algorithm dependent on the application. The compression proportion is the proportion of the size of the original and the reproduced image. Error versatility is the level of spectral error regulation of the algorithm. Multifaceted nature is the running time of programming usage of the algorithm.

Performance Parameters of Compression Techniques

The compression techniques use a wide number of performance measures to compute their efficiency and performance. The metrics used to compute the performance are:

Peak Signal-to-Noise Ratio

The PSNR is defined as the ratio of the maximum pixel intensity to the mean square error (Abo-Zahhad et al., 2015). The formula used to compute the PSNR is represented as follows:

f 2s -0

PSNR = 20/og ----i dB (6.1)


Here, B is the number of bits and MSE is the mean square error.

Compression Ratio

The CR is the ratio of the size of the original image to the size of the compressed image. It can be computed as

„„ Size of the original image C A — -------------------------

Size of the compressed image

Mean Square Error

MSE is the description of the cumulative squared error between the compressed image and the original image (Karthikeyan and Thirumoorthi, 2016). It can be computed with the use of the following formula:

Structural Similarity Index

The SSIM is used to measure the tendency of similarity between the original image and the compressed image (Eben Sophia and Anitha. 2017). The formula used to find the SSIM is:


y Px + Pi "I" C1 J ^<5,v + Öx + C?2 j

Here, x is the original image, y is the reconstructed image, C1 and C2 are the constants, p is the average gray value, and S is the variance

Bits per Pixel

BPP is defined as the ratio of the total size of the compressed image to the total number of the pixel in the image (Perumal and Pallikonda Rajasekaran, 2016).


Size of the compressed image Total no. of pixel in the image


6(number of bits)


  • (6.5a)
  • (6.5b)

where m and n are image dimensions and size is the dimensions of the compressed image.

Signal-to-Noise Ratio

The SNR can be defined as the ratio of the signal power to the noise power. It is measured in dB and can be computed as

SNR = log-


Or SNR = logl0Ey


where Ex and EY are the energy of the original image signal and reconstruction error image signal, respectively.

Percent Rate of Distortion

It is the measure of the distortion in the reconstructed image. The lesser the value of the PRD, the less distorted the reconstructed image (Selvi et al.. 2017). It can be computed with the use of the following formula:

Here, M x N represents size of the image, fix, y) is the original image, and f (x, y) is the reconstructed image.

Correlation Coefficient

It is used to describe the existing correlation between the original image and the reconstructed image (Selvi et al., 2017). It can be calculated by.

Here, M x N represents size of the image, fix, y) is the original image, and f (x, y) is the reconstructed image

Structural Content

It is used to depict the comparison between two images inherited in small patches and to determine the common things images have (Karthikeyan and Thirumoorthi, 2016). The higher the value of SC, the poorer the quality of the image.


Here, M and N are the dimensions of the image.

Applications of Compression Techniques

Some of the application areas of compression techniques are as follows (Figure 6.4):

Satellite Images

Satellite images are one of the most impressive and significant apparatuses utilized by the meteorologist. They are basically the eyes in the sky. These images console forecasters to the conduct of the environment as they give a reasonable, brief, and exact portrayal of how situations are developing. Estimating the climate and directing exploration would be amazingly troublesome without satellites. Information taken at stations around the nation is constrained in its portrayals of barometrical

Compression techniques

FIGURE 6.4 Compression techniques.

movement. It is as yet conceivable to carry out a decent investigation from the information, but since the stations are isolated by several miles huge highlights can be missed. Satellite images help in indicating what cannot be estimated or seen. Likewise, the satellite images are seen as truth. There is no possibility of the blunder. Satellite images give information that can be deciphered "direct".

Broadcast Television

Computerized broadcasting gives superior quality, high-caliber, and better-communicating administration, supplanting conventional simple telecom. The advanced telecom is grouped into satellite-computerized broadcasting and earthbound advanced telecom.

Genetic Images

Imaging genetic qualities is an interesting technique to survey the effect of genetic factors on both the cerebrum structure and capacity. All the more significantly, imaging hereditary qualities manufacture a scaffold to comprehend the social and clinical ramifications of hereditary qualities and neuroimaging. By portraying and evaluating the cerebrum estimates influenced in mental scatters, imaged genetic qualities are adding to distinguishing potential biomarkers for schizophrenia and related issues.

Internet Telephony and Teleconferencing

Internet telephony or telecommunications offers the opportunity to design a global multimedia communication system that can eventually replace existing telephony infrastructure. It has upper-layer protocol components that are specific to Internet telephony services: real-time transport protocol (RTP) for carrying voice and video data, data streams as required, and Session Initiative Protocol (SIP) for signals. Some complementary protocols, including the Real-Time Streaming Protocol (RTSP) for control of streaming media and the Wide-Area Service Discovery Protocol (WASRV) for the location of telephony gateways.

Electronic Health Records

The electronic health record (EHR) at that point called the electronic medical record (EMR) or computerized patient record. The Electronic Health Record (EHR) is about quality, wellbeing, and proficiency. An EHR is an electronic rendition of a patient's clinical history, that is kept up by the supplier after some time, and may incorporate the entirety of the key managerial clinical information applicable to that people care under a specific supplier, including socioeconomics, progress notes, issues, meds, imperative signs, past clinical history, vaccinations, research center information, and radiology reports. The EHR computerizes access to data and can possibly smooth out the clinician's work process. The EHR likewise can bolster other consideration-related exercises legitimately or by implication through different interfaces, including proof-based choice help, quality administration, and result announcement.

Computer Communication

Communication is the exchange of information or data between at least two things, for example, individuals, gadgets, governments, associations, or organizations. In advanced correspondence, information is traded between at least two processing gadgets.

Remote Sensing via Satellites

Remote sensors gather information by identifying the vitality that is reflected from Earth. These sensors can be on satellites or mounted on airplanes. Remote sensors can be either aloof or dynamic. Inactive sensors react to outside improvements. They record common vitality that is reflected or transmitted from the Earth's surface. The most well-known wellspring of radiation identified by detached sensors is reflected in daylight.


Abo-Zahhad, M.. Gharieb. R.R., Ahmed. S.M.. and Abd-Ellah. M.K. (2015). Huffman image compression incorporating DPCM and DWT. Journal of Signal and Information Processing 6(02), 123.

Amri. H.. Khalfallah. A.. Gargouri, M.. Nebhani, N.. Lapayre. J.-C.. and Bouhlel, M.-S. (2017). Medical image compression approach based on image resizing, digital watermarking and lossless compression. Journal of Signal Processing Systems 87(2). 203-214.

Anusuya. V., Srinivasa Raghavan. V.. and Kavitha, G. (2014). Lossless compression on MRI images using SWT. Journal of Digital Imaging 27(5). 594-600.

Arif. A.S.. Mansor, S.. Logeswaran. R.. and Abdul Karim. H. (2015). Auto-shape lossless compression of pharynx and esophagus fluoroscopic images. Journal of Medical Systems 39(2), 5.

Avramovic. A. and Banjac. G. (2012). On predictive-based lossless compression of images with higher bit depths. Telfor Journal 4(2), 122-127.

Ayoobkhan. M.U.A., Chikkannan, E.. and Ramakrishnan, K. (2017). Lossy image compression based on prediction error and vector quantisation. EURASIP Journal on Image and Video Processing 1, 35.

Bairagi, V.K. (2015). Symmetry-based biomedical image compression. Journal of Digital Imaging 28(6). 718-726.

Bairagi, V.K.. Sapkal, A.M.. and Tapaswi, A. (2013). Texture-based medical image compression. Journal of Digital Imaging 26( 1), 65-71.

Bouslimi, D.. Coatrieux. G.. Quantin, C.. Allaert, F.A.. Cozic. M.. and Roux. C. (2015). A teleassistance protocol based on joint watermarking-encryption evidence for identification of liabilities in case of litigation. IRBM 36(5), 279-286.

Brahimi. T.. LarbiBoubchir. R.F.. and Nait-Ali. A. (2017). An improved multimodal signalimage compression scheme with application to natural images and biomedical data. Multimedia Tools and Applications 76(15). 16783-16805.

Bruylants. T, Munteanu. A., and Schelkens. P. (2015). Wavelet based volumetric medical image compression. Signal Processing: Image Communication 31.112-133.

Cyriac, M. and Chellamuthu, C. (2012). A novel visually lossless spatial domain approach for medical image compression. European Journal of Scientific Research 71(3). 347-351

Das, S. and Kundu. M.K. (2013). Effective management of medical information through ROL lossless fragile image watermarking technique. Computer Methods and Programs in Biomedicine 111(3). 662-675.

Eben Sophia, P. and Anitha, J. (2017). Contextual medical image compression using normalized wavelet-transform coefficients and prediction. IETE Journal of Research 63(5), 671-683.

Haddad, S.. Coatrieux, G.. Cozic, M.. and Bouslimi. D. (2017). Joint watermarking and lossless JPEG-LS compression for medical image security. IRBM 38(4), 198-206.

Hagag, A.. Hassan, E.S.. Amin. M.. El-Samie. F.E.A.. and Fan. X. (2017). Satellite multispec-tral image compression based on removing sub-bands. Optik 131, 1023-1035.

Hosseini, S.M. and Naghsh-Nilchi, A.-R. (2012). Medical ultrasound image compression using contextual vector quantization. Computers in Biology and Medicine 42(7), 743-750.

Hsu. W.Y. (2015). Segmentation-based compression: New frontiers of telemedicine in telecommunication. Telematics and Informatics 32(3), 475-485.

Hsu. W.Y. (2017). Clustering-based compression connected to cloud databases in telemedicine and long-term care applications. Telematics and Informatics 34(1). 299-310.

Hsu. W.-Y. (2012). Improved watershed transform for tumor segmentation: application to mammogram image compression. Expert Systems with Applications 39(4), 3950-3955.

Huang. K.K.. Liu. H., Ren, C.X.. Yu. Y.F., and Lai. Z.R. (2017). Remote sensing image compression based on binary tree and optimized truncation. Digital Signal Processing 64. 96-106.

Ibraheem, M.S., Zahid Ahmed. S.. Hachicha, K., Hochberg. S., and Garda. P. (2016). Medical images compression with clinical diagnostic quality using logarithmic DWT. In 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 402-405. IEEE.

Jiang. H.. Ma, Z., Hu. Y. Yang. B.. and Zhang. L. (2012). Medical image compression based on vector quantization with variable block sizes in wavelet domain. Computational Intelligence and Neuroscience 2012, 5.

Jiang. N.. Zhuang. Y. and Chiu. D.K. (2017). Multiple transmission optimization of medical images in recourse-constraint mobile telemedicine systems. Computer Methods and Programs in Biomedicine 145. 103-113.

Juliet. S.. Rajsingh. E.B.. and Ezra, K. (2016). A novel medical image compression using Ripplet transform. Journal of Real-Time Image Processing 11(2). 401-412.

Karthikeyan T. and Thirumoorthi, C. (2016). A hybrid medical image compression techniques for lung cancer. Indian Journal of Science and Technology (LIST) 9(39), 1-6.

Lucas. L.F.R.. Rodrigues. N.M.M.. da Silva Cruz. L.A.. and de Faria. S.M.M. (2017). Lossless compression of medical images using 3-d predictors. IEEE Transactions on Medical Imaging 36(11), 2250-2260.

Mofreh. A.. Barakat. T.M., and Refaat. A.M. (2016). A new lossless medical image compression technique using hybrid prediction model. Signal Processing: An International Journal (SPIJ) 10(3), 20.

Nasr. K.M. and Martini. M.G. (2017). A visual quality evaluation method for telemedicine applications. Signal Processing: Image Communication 57. 211-218.

Parikh. S.S., Ruiz. D., Kalva, H.. Fernandez-Escribano, G.. and Adzic. V. (2017). High bitdepth medical image compression with HEVC. IEEE Journal of Biomedical and Health Informatics 22(2). 552-560.

Perumal, B. and Pallikonda Rajasekaran. M. (2016). A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression. In 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS). pp. 1-5. IEEE.

Perumal, B. and Rajasekaran. M.P. (2016. February). A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression. In 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) (pp. 1-5). IEEE.

Prabhu. K.M.M., Sridhar. K., Mischi, M., and Bharath H.N. (2013). 3-D warped discrete cosine transform for MRI image compression. Biomedical Signal Processing and Control 8(1), 50-58.

Ramesh. S.M. and Shamnugam, A. (2010). Medical image compression using wavelet decomposition for prediction method. arXiv preprint arXiv: 1002.2418.

Rani. M.. Shanthi, M.. and Chitra, P. (2018). A hybrid medical image coding method based on Haar wavelet transform and particle swarm optimization technique. International Journal of Pure and Applied Mathematics 118( 18), 3059-3067.

Raza. M.. Adnan, A.. Sharif. M.. and Waqas Haider, S. (2012). Lossless compression method for medical Image sequences using super-spatial structure prediction and Inter-frame coding. Journal of Applied Research and Technology 10(4), 618-628.

Rufai, A.M.. Anbarjafari. G., and Demirel, H. (2013). Lossy medical image compression using Huffman coding and singular value decomposition. In 2013 21st Signal Processing and Communications Applications Conference (SIU), pp. 1-4. IEEE.

Sanchez. V., Abugharbieh, R., and Nasiopoulos, P. (2010). 3-D scalable medical image compression with optimized volume of interest coding. IEEE Transactions on Medical Imaging 29(10), 1808-1820.

Sandhu. A.K. and Kaur. E.A. (2015). Genetically Modified Compression Approach for Multimedia Data on Cloud Storage.

Selcan, D.. Kirbis. G.. and Kramberger, I. (2015). FPGA-based CCSDS compliant miniaturized satellite communication stack. IFAC-PapersOnLine 48(10), 28-33.

Selvi, G.U.V. and Nadarajan, R. (2017). CT and MRI image compression using wavelet-based contourlet transform and binary array technique. Journal of Real-Time Image Processing 13(2). 261-272.

Selvi, G.. Uma. V.. and Nadarajan. R. (2017). CT and MRI image compression using waveletbased contourlet transform and binary array technique. Journal of Real-Time Image Processing 13(2), 261-272.

Shi, C., Zhang. J., and Zhang. Y. (2016). Content-based onboard compression for remote sensing images. Neurocomputing 191, 330-340.

Sotnassoundaram. T. and Subramaniam, N.P. (2018). High performance angiogram sequence compression using 2D bi-orthogonal multi wavelet and hybrid speck-deflate algorithm. Biomedical Research (0970-938X).

Spelic. D. and Zalik, B. (2012). Lossless compression of threshold-segmented medical images. Journal of Medical Systems 36(4). 2349-2357.

Srinivasan. K.. Dauwels. J., and Ramasubba Reddy. M. (2011). A two-dimensional approach for lossless EEG compression. Biomedical Signal Processing and Control 6(4), 387-394.

Sriraain. N. and Shyatnsunder. R. (2011). 3-D medical image compression using 3-D wavelet coders. Digital Signal Processing 21(1). 100-109.

Taquet. J. and Labit. C. (2012). Hierarchical oriented predictions for resolution scalable lossless and near-lossless compression of CT and MRI biomedical images. IEEE Transactions on Image Processing 21(5), 2641-2652.

Thomas. D.S., Moorthi. M.. and Muthalagu, R. (2014). Medical image compression based on automated ROI selection for telemedicine application. International Journal of Engineering and Computer Science 3, 3638-3642.

Vaishnav, M.. Kamargaonkar. C.. and Sharma, M. (2017). Medical image compression using dual tree complex wavelet transform and arithmetic coding technique. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2(3).

Xiao. B.. Lu. G., Zhang, Y. Li, W. and Wang, G. (2016). Lossless image compression based on integer discrete Tchebichef transform. Neurocomputing 214. 587-593.

Zuo, Z.. Lan. X.. Deng. L.. Yao, S., and Wang. X. (2015). An improved medical image compression technique with lossless region of interest. Optik 126(21). 2825-2831.

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