Coronary angiography is an X-ray examination of the heart\u27s arteries. This is an essential technique for diagnosis of heart damages. Image sequences from digital angiography contain areas of high diagnostic interest. Loss of information due to compression for regions of interest (ROI) in angiograms is not tolerable. Since Commercially available technology such as JPEG and MPEG do not satisfy medical requirements due to their severe blockartifacts. In this paper, a new compression algorithm that achieves high compression ratio and excellent reconstruction quality for video rate or sub-video rate angiograms is developed. The proposed algorithm exploits temporal spatial and spectral redundancies in backward adaptive fashion with Extremely low side information. An experimental result shows that the proposed scheme provides significant improvements in compression efficiencies

BHAVANI, P.

KALYAN, P.

MURTY, T. VISHNU

English

Interscience Research Network

International Journal of Electronics Signals and Systems Volume 3 Issue 2 Article 8 October 2013 MICROANGIOGRAM VIDEO COMPRESSION USING ADAPTIVE PREDICTION T. VISHNU MURTY Department of Electronics and Communications Engineering, Sri Sivani, College of Engineering,Chilakapalem, Srikakulam, Andhra Pradesh, India., vishnu.tammineni@gmail.com P. KALYAN Department of Electronics and Communications Engineering, Sri Sivani, College of Engineering,Chilakapalem, Srikakulam, Andhra Pradesh, India., kalyanecebujji@gmail.com P. BHAVANI Department of Electronics and Communications Engineering, Sri Sivani, College of Engineering,Chilakapalem, Srikakulam, Andhra Pradesh, India., bhavani_etc@yahoo.com Follow this and additional works at: https://www.interscience.in/ijess Digital Commons Network Logo  Part of the Electrical and Electronics Commons Recommended Citation MURTY, T. VISHNU; KALYAN, P.; and BHAVANI, P. (2013) "MICROANGIOGRAM VIDEO COMPRESSION USING ADAPTIVE PREDICTION," International Journal of Electronics Signals and Systems: Vol. 3 : Iss. 2 , Article 8. DOI: 10.47893/IJESS.2013.1150 Available at: https://www.interscience.in/ijess/vol3/iss2/8 This Article is brought to you for free and open access by the Interscience Journals at Interscience Research Network. It has been accepted for inclusion in International Journal of Electronics Signals and Systems by an authorized editor of Interscience Research Network. For more information, please contact sritampatnaik@gmail.com. MICROANGIOGRAM VIDEO COMPRESSION USING  ADAPTIVE PREDICTION  T. VISHNU MURTY. 1, P.KALYAN2 & P.BHAVANI3   1,2&3Department of Electronics and Communications Engineering, Sri Sivani, College of Engineering,Chilakapalem, Srikakulam, Andhra Pradesh, India. Email: Vishnu.tammineni@gmail.com, kalyanecebujji@gmail.com,bhavani_etc@yahoo.com.   Abstract: Coronary angiography is an X-ray examination of the heart's arteries. This is an essential technique for diagnosis of heart damages. Image sequences from digital angiography contain areas of high diagnostic interest. Loss of information due to compression for regions of interest (ROI) in angiograms is not tolerable. Since Commercially available technology such as JPEG and MPEG do not satisfy medical requirements due to their severe blockartifacts. In this paper, a new compression algorithm that achieves high compression ratio and excellent reconstruction quality for video rate or sub-video rate angiograms is developed. The proposed algorithm exploits temporal spatial and spectral redundancies in backward adaptive fashion with Extremely low side information. An experimental result shows that the proposed scheme provides significant improvements in compression efficiencies.   1. ANGIOGRAM VIDEO  Recent advances in imaging technology have directly benefited diagnostic and corrective medicine. In the field of angiography, currently applied technology  allows for the capture of an X-ray video of the heart which can be examined and processed in real-time. The current trend towards using digital representations of such data provides many tangible benefits over more traditional analogue-based approaches. These include the increased control over  playback, identification and examination of key frames in the sequence. Also of benefit is the ability to apply image processing techniques to, for example, estimate the extent of a restriction of a coronary artery. Furthermore the digital representation of the  Data allows for a more convenient approach to data storage and transmission. Due to the nature of the data however, and the typical duration of a procedure, the resulting uncompressed angiogram video is likely to be of the order of Mbytes in size. This can cause some difficulties with the storage of the data, with particular problems concerning the transmission of the video data elsewhere, especially if the transmission bandwidth available is low. This problem would be alleviated if an efficient means of compression could be found.  1.1. THE NEED FOR COMPRESSION Unfortunately, the logistics concerning angiogram video, as with much medical imaging, makes such a vision hard to realize in practice. Typical image sizes are 512x512 (or even 1024x1024) pixels, taken at 30 frames/sec with at least 8 bits of resolution. A typical procedure may be of the order of 5 minutes resulting in approximately 2.5 GBytes of raw data. At a constant data rate of 64Kbit/sec it would take 80  hours to transmit this quantity of data, and even at 10Mb/sec it would take 30 minutes. From this it is clear that a practical system for transmitting angiogram video from one hospital to another    requires a high bandwidth connection, but even then a wait of 30 minutes may be considered unacceptable, especially if a semi-real-time diagnosis is required. Matters may be improved considerably by compressing the video stream. Due to the sensitivity of the data however, only lossless or near-losslessalgorithms can realistically be used. Unfortunately, the tight constraints imposed by lossless compression usually limit the compression ratio to about 2 or 3:1. For diagnostic purposes, it is essential that the compression process causes no tangible loss of detail and introduces no noticeable artifacts which could be misinterpreted as being pathological in nature. Little research has been done specifically regarding the compression of angiogram video. Unfortunately, given the unique structure of the data, video compression results relating to more conventional types of video (e.g. for digital television), for which there has been a great deal of research, do not necessarily apply here.   1.2. INTRODUCTION  DUE to its importance in multimedia applications, most research on video compression has centered on lossy video compression where the focus is on achieving a good tradeoff between the reconstructed quality and the compression ratio. Current lossy video compression provides substantial compression efficiency at the cost of minimal degradation of quality. Historically, significantly less interest has been paid to the development of lossless video compression algorithms. Lossless video compression is important to applications in which the video quality  cannot tolerate any degradation, such as archiving of a video, compression of medical and satellite videos, etc. Recently, there has been increasing interests in developing lossless video compression techniques              International Journal of Electronics Signals and Systems (IJESS) ISSN: 2231-5969, Vol-3, Iss-2111[1]–[9].Currently, there are several methods availableMicroangiogram Video Compression Using  Adaptive Prediction for digital image compression. In applications where some information loss is acceptable, lossy methods are often used, because these tend to be have higher coding efficiency. However, in applications such as medical imaging, remote sensing and legal imaging, the data validity and precision must be preserved for subsequent reconnaissance; therefore lossless approaches tend to be more adequate. High image resolutions and depths are ubiquitous in medical imaging, and such images usually are stored for long periods of time for legal reasons. Lossless image compression methods that explore correlations among spatially adjacent pixels are popular now a day. Such  methods have been applied in static image lossless coding, and can be divided into three categories: transform-based methods, methods based on predictors, and multi-resolution techniques. Dynamic images like videos usually are redundant in the spatial  and in the temporal sense (i.e. pixels in adjacent frames tend to be correlated to some degree). Thus, compression techniques for videos often explore the spatial and temporal redundancy in the data (e.g. MPEG-2 [5], H.264 [6] and others [7]), work in this direction.  Prediction based schemes have been successful in lossless coding of still medical images, such as JPEG-LS [8]  andCALIC [9].   2.0 PIXEL BASED PREDICTION  It is well known that the choice of block-size used in motion compensation is always a tradeoff between prediction accuracy and the size of side information. Smaller and more numerous blocks can provide smaller prediction residuals but result in more side information and more motion vectors. Choosing a large block size means a small number of bits for the side information, but also lower prediction efficiency resulting in a larger prediction residual. Even in the scheme proposed in [9], which requires no transmission of motion vectors, there still exists a similar tradeoff between the prediction accuracy and the size of side information. By considering the above  tradeoff, we introduce a new lossless coding scheme with two key points: It adopts a pixel-based motion compensation concept in order to minimize the prediction residual, and it only needs to transmit fixed size side information, which is also extremely low compared to the size of the original video sequence.This way of prediction makes the adaptive to be the best compared to previous algorithms (i.e. JPEG, CALIC).  In this section, we present the proposed adaptive pixel-based prediction scheme which exploits the redundancies in the wavelet domain or in the spatial domain with fixed size side information. As shown  in Fig. 1, the proposed scheme consists of the following main steps: preprocessing, adaptive symbol prediction, adaptive prediction mode selection, and context-based arithmetic coding of the prediction residuals. Preprocessing aims to determine the operational domain of temporal and spatial redundancies. The candidate operational domains are the wavelet domain and the spatial domain. In the adaptive symbol prediction step, three separate predictors are utilized to reduce the spatial and temporal redundancies: a novel temporal predictor, a spatial predictor and a direct predictor. In the adaptive prediction mode selection step, a predictor can be selected adaptively from three candidates based on the causal previous predictive information to maximize the prediction performance.   2.1. SPATIAL PREDICTION  To reduce the spatial redundancy, a prediction is computed based on the neighboring symbols in the same frame as the symbol to be encoded (here, we use the term symbol because it can be a pixel in the spatial domain or a wavelet coefficient in the wavelet domain). In the proposed scheme, we use a simple but robust spatial predictor, the median edge detector MED), as used in JPEG-LS [11].MED estimates the  symbolto be encoded based on the values of the three previously encoded neighboring symbols. We use p(x,y) to represent the symbol to be encoded that is located at (x,y) in frame . The spatial predicted value of p(x,y) is represented as pi(x,y), which is given by   Thus spatial prediction residual is             International Journal of Electronics Signals and Systems (IJESS) ISSN: 2231-5969, Vol-3, Iss-2112Microangiogram Video Compression Using  Adaptive Prediction    2.2. TEMPORAL PREDICTION  Let pi(x,y) be the symbol to be encoded. The proposed temporal predictor aims to find the best matched symbol in reference frame [i-1],which is denoted as the temporal predictor piT(x,y).Instead of piT(x,y) exploiting the motion activity of between adjacent frames directly, the predictor investigates the motion activity of the target  window of pi(x,y)  in frame i and frame [i-1] within a search range W*H as illustrated in Fig.4 ,where the target window is composed of the upper-left neighboring symbols of p(x,y)   The temporal predictor of symbol pi(x,y) searches and locates the best matched target  window in frame [i-1]  which achieves the minimum cumulative absolute difference (CAD) within the search range, where where d denotes the target window, and pi(x,y),pi-1(x,y) the symbol values of the current frame and the reference frame , respectively, and where a motion vector (m,n) is determined for the region -W  TO minimize the  CAD Similar to block motion compensation techniques, the best motion vector for the target window  with the minimum CAD is determined by  target window with the minimum CAD is determined by (mo,no) =arg{ min(CAD(d)}Where (mo,no)  indicates the motion displacement of the target window pi(x,y). Then, the temporal predictor of can be obtained bypi T (x,y) =pi(x+m,y+n)              (3) Where temporal error is         e2 = pi(x,y)-pi T (x,y)                     (4)  2.3. DIRECT MODE If a video sequence is to be processed in the wavelet domain, then we use another prediction mode which is similar to the concept of direct sending mode as described in [9]. Because of the energy compaction property of the IWT, the wavelet coefficients in the high frequency sub bands (LH, HL, HH) usually have small amplitudes, which may be smaller than the amplitudes of the spatial prediction residuals and temporal prediction residuals. Therefore, in this case the wavelet coefficients are encoded and transmitted  directly denoted as e3.  2.4. ADAPTIVE PREDICTION MODE  SELECTION This is accomplished by utilizing a simple but effective backward adaptive prediction mode selector. The scheme adaptively selects the predictor among three candidates (e1, e2, or e3 ) based on previous prediction accuracy.  Fig.5 .Final errors:e1,e2,e3 video1 2.5 CONTEXT MODELLING  Context modelling is used for efficient coding of the prediction residuals. By utilizing suitable context             International Journal of Electronics Signals and Systems (IJESS) ISSN: 2231-5969, Vol-3, Iss-2113Microangiogram Video Compression Using  Adaptive Prediction models, the given prediction residual can be encoded by switching between different probability models according to already encoded neighboring symbols of the symbol to be encoded.  3. RESULTS  We tested the proposed scheme using standard color video sequences;  all experiments were conducted to get the best performance.  The proposed scheme with  JPEG-LS, CALIC, on various YUV and RGB sequences is better, which verify that our approach achieves significant performance enhancement with respect to JPEG-LS, CALIC in terms of bit rate. The  table1 show the bite rate and compression ratio for different video sequences for the proposed algorithm.  Table1: Results of bite rates and compression ration for various video sequences  4. CONCLUSIONS  In this paper, a new scheme for lossless coding medical video sequences is presented. It exploits spectral, spatial, and temporal redundancies and adaptively selects the best predictor out of a set of  predictors without using any side information. The results show a good compression ratios and better bit rate compared to previous compression  algorithms.  5. REFERENCES  [1] N. D. Memon and K. Sayood, “Lossless  compression of video sequences,” IEEE  Trans. Commun., vol. 44, no. 10, pp. 1340– 1345, Oct. 1996. [2]  K. H. Yang and A. F. Faryar, “A context-based predictive coder for lossless nd near-lossless compression of video,” in Proc. Int. Conf.  Image Processing, Sep. 2000, vol. 1, pp. 144–147. [3]  E. S. G. Carotti, J. C. De Martin, and A.  R. Meo, “Low-complexity lossless video coding via spatio-temporal prediction,” in Proc. Int. Conf.  Image Processing, Sep. 2003, vol. 2, pp. 197–200. [4], ------“Backward- adaptive lossless  compression of video sequences ,in Proc.   IEEE Int. Conf. Audio, Speech, Signal   Processing, Apr. 2002, pp. 3417–3420. [5]  D. Brunello, G. Calvagno, G. A. Mian,  and R. Rinaldo, “Lossless compression of  video using temporal information,” IEEE Trans. Image  Process., vol. 12, no. 2, pp. 132–139, Feb. 2003. [6]  B. Martions and S. Forch gammer, “Lossless compression of video using  motion compensation,” in Proc. IEEE DCC, 1998, pp. 560–589. [7]  Z. Ming-Feng, H. Jia, and Z. Li-Ming,  “Lossless video compression using  Angiogram videos Original bits Compressed  bits Bits/ pixelCompressio n ratio 1 2918400 426956 1.1700.11022 1843200 174198 0.7560.09453 3110400 85255 0.219  0.0274combination of temporal and spatial  prediction,” in Proc.  IEEE. Int. Conf.  Neural Networks Signal Processing, Dec. 2003, pp.1193–1196. [8]  Y. Gong, S. Pullalarevu, and S. Sheikh,  “A wavelet-based lossless video coding  scheme,” in Proc. Int. Conf. Signal   Processing, 2004, pp. 1123–1126. [9] S.-G. Park, E. J. Delp, and H. Yu,  “Adaptive lossless video compression using  an integer wavelet transform,” in Proc. Int.   Conf. Image Processing, 2004, pp. 2251– 2254. [10]  S.-G. Park, “Adaptive lossless video  compression,” Ph.D. dissertation, School  Elect. Comput. Eng., Purdue Univ.,West  Lafayette, IN, Dec.2003.[11] JPEG-LS, Lossless and Near-Lossless  Coding of Continuous Tone Still Images,  ISO/IEC JTC1/SC 29/WG 1, Jul. 1997. [12]  N. Memon and X. Wu, “Context-based,adaptive, lossless image coding,” IEEE  Trans. Commun., vol. 45, no. 4, pp. 437–444, Apr.1997. [13] A. R. Calderbank, I. Daubechies, W.  Sweldens, and B. L. Yeo,“Wavelet  transforms that map integers to integers,” Appl. Comput.Harmon. Anal., vol. 5, no. 3,  pp. 332–369, Jul. 1998. [14]  “Lossless image compression using  integer to integer wavelet transforms,” in  Proc. IEEE Int. Conf. Image Processing Oct. 1997, vol.1, pp. 596–599. [15]  K. Sayood, Introduction to Data  Compression, 2nd ed. San Mateo, CA:  Morgan Kaufmann, 2000.  [16] D. S. Taubman and M. W. Marcellin, JPEG2000 Image  ompression  Fundamentals Standards and Practice. Norwell, MA: Kluwer, 2002. AUTHORS:  1.  T. Vishnu murty  is pursuing his M.Tech degree in the Department of Electronics & Communications (DECS), Sri Sivani College of Engineering, Chilakapalem,  Srikakulam, and India. His areas of interests  are digital video processing,  communications.  2. P. Kalyan has Obtained B. Tech. in ECE Department from Sarada Institute of Science technology and Management, Srikakulam and .Tech in Electronic from NIT. He has Three years of teaching experience, presently working at Sri Sivani College of Engineering, Chilakapalem, and Srikakulam as Assistant Professor in Department of ECE. 3.  P.Bhavani  is pursuing his M.Tech degree in the Department of Electronics &  Communications (DECS), Sri Sivani  College of Engineering, Chilakapalem, Srikakulam, and India. His areas of interests are digital video processing, communications.                International Journal of Electronics Signals and Systems (IJESS) ISSN: 2231-5969, Vol-3, Iss-2114

MICROANGIOGRAM VIDEO COMPRESSION USING ADAPTIVE PREDICTION

https://www.interscience.in/cgi/viewcontent.cgi?article=1150&context=ijess

MICROANGIOGRAM VIDEO COMPRESSION USING ADAPTIVE PREDICTION

Abstract

Similar works

Full text

Available Versions

Interscience Research Network