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Medical Video Coding Based on 2Nd-Generation Wavelets: Performance Evaluation

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Medical Video Coding Based on 2Nd-Generation Wavelets: Performance Evaluation electronics Article Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation Merzak Ferroukhi 1, Abdeldjalil Ouahabi 2,3,* , Mokhtar Attari 1, Yassine Habchi 4 and Abdelmalik Taleb-Ahmed 5 1 Laboratory of Instrumentation, Faculty of Electronics and Computers, University of Sciences and Technology Houari Boumediene, 16111 Algiers, Algeria; [email protected] (M.F.); [email protected] (M.A.) 2 Polytech Tours, Imaging and Brain, INSERM U930, University of Tours, 37200 Tours, France 3 Computer Science Department, LIMPAF, University of Bouira, 10000 Bouira, Algeria 4 Electrical Engineering Department, LTIT, Bechar University, 08000 Bechar, Algeria; [email protected] 5 IEMN DOAE UMR CNRS 8520, UPHF, 59313 Valenciennes, France; [email protected] * Correspondence: [email protected]; Tel.: +33-603894463 Received: 5 October 2018; Accepted: 8 January 2019; Published: 14 January 2019 Abstract: The operations of digitization, transmission and storage of medical data, particularly images, require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. The 2nd-generation wavelets seeks to be positioned and confronted by the image and video coding methods currently used. It is in this context that we suggest a method combining bandelets and the SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage of capturing the geometrical complexity of the image structure. The second reason is the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements indicate that in some cases (for low bit rates) the performance of the proposed coding competes with the well-established ones (H.264 or MPEG4 AVC and H.265 or MPEG4 HEVC) and opens up new application prospects in the field of medical imaging. Keywords: bandelets; medical imaging; quadtree decomposition; SPIHT coder; video coding; MPEG4 AVC; HEVC; video quality measure 1. Introduction and Motivation The extensive volume of patient medical data recorded at every moment in hospitals, medical imaging centers and other medical organizations has become a major issue. The need for quasi-infinite storage space and efficient real-time transmission in specific applications such as medical imaging, military imaging and satellite imaging requires advanced techniques and technologies including coding methods to reduce the amount of data to be stored or transmitted. Without coding of information, it is very difficult and sometimes impossible to make a way to store or communicate big data (large volumes of high velocity and complex images, audio and video information) via the internet. The encoding of these data is obtained by eliminating the redundant or unnecessary information in the original frame which cannot be identified with the naked eye. Telemedicine offers valuable specialty diagnostic services to underserved patients in rural areas, but often requires significant image compression to transmit medical images using limited bandwidth Electronics 2019, 8, 88; doi:10.3390/electronics8010088 www.mdpi.com/journal/electronics Electronics 2019, 8, x FOR PEER REVIEW 2 of 18 bandwidth networks. The difficulty is that compressing images such as those for tele-echography can introduce artifacts and reduce medical image quality which affects directly the diagnosis [1]. Electronics 2019, 8, 88 2 of 18 This possible disadvantage is inherent in all lossy compression methods. Figure 1 recalls the key steps of video coding. Transformation is a linear and reversible operationnetworks. Thethat difficultyallows the is thatimage compressing to be represented images such in asthe those transform for tele-echography domain where can the introduce useful informationartifacts and is reduce well localized. medical image This property quality which will make affects it possible directly theduring diagnosis the compression [1]. to achieve goodThis discrimination, possible disadvantage that is to say is inherent the suppressi in all lossyon of compressionunnecessary methods.or redundant information. The discreteFigure cosine1 recalls transform the key stepsand the of videowavelet coding. tran Transformationsform are among is a linearthe most and reversiblepopular transforms operation proposedthat allows for the image image and to video be represented coding. in the transform domain where the useful information is wellQuantization localized. is This the step property of the willcompression make it possibleprocess during during which the compression a large part of to the achieve elimination good ofdiscrimination, unnecessary or that redundant is to say theinformation suppression occurs. of unnecessary or redundant information. The discrete cosineCoding transform techniques and the e.g., wavelet Huffman transform coding areand among arithmetic the mostcoding popular [2], are transforms the best-known proposed entropy for codingimage andparticularly video coding. in image and video coding. Figure 1. SimplifiedSimplified video coding scheme. ItQuantization is well known is the that step lossless of the compression compression methods process duringhave the which advantage a large of part providing of the elimination an image withoutof unnecessary any loss or redundantof quality informationand the disadvantage occurs. of compressing weakly. Hence, this type of compressionCoding techniquesis not suitable e.g., for Huffman transmission coding or and storage arithmetic of big coding data such [2], areas video. the best-known Conversely, entropy lossy compressioncoding particularly produces in image significantly and video smaller coding. file sizes often at the expense of image quality. However,It is well lossy known coding that based lossless on transforms compression has methods gained great have importance the advantage when of several providing applications an image withouthave appeared. any loss In of particular, quality and lossy the coding disadvantage represents of compressingan acceptable weakly. option Hence,for coding this medical type of images.compression e.g., a is DICOM not suitable (Digital for Imaging transmission Communica or storagetion ofin bigMedicine) data such JPEG as (Joint video. Photographic Conversely, Expertslossy compression Group) based produces on discrete significantly cosine smallertransform file (DCT) sizes oftenand a at DICOM the expense JPEG of 2000 image based quality. on wavelet.However, In lossy this context, coding basedwavelet-based on transforms image has codi gainedng, e.g., great JPEG2000, importance using when multiresolution several applications analysis [3,4]have exhibits appeared. performance In particular, that lossy is highly coding superior represents to other an acceptable methods optionsuch as for those coding based medical on DCT, images. e.g., JPEG.e.g., a DICOM (Digital Imaging Communication in Medicine) JPEG (Joint Photographic Experts Group) basedIn on 1993, discrete Shapiro cosine introduced transform a (DCT) lossy andimage a DICOM compression JPEG 2000 algorithm based onthat wavelet. he called In thisembedded context, wavelet-basedzerotrees of wavelet image coding,(EZW) e.g.,[5]. JPEG2000,The success using of co multiresolutionding wavelet analysisapproaches [3,4 ]is exhibits largely performance due to the occurrencethat is highly of superior effective to sub-band other methods coders. such The as thoseEZW basedencoder on DCT,is the e.g., first JPEG. to provide remarkable distortion-rateIn 1993, Shapiro performance introduced while aallowing lossy image progressive compression decoding algorithm of the that image. he calledThe principle embedded of zerotrees,zerotrees ofor waveletother partitioning (EZW) [5]. structures The success in sets of codingof zeros, wavelet makes approachesit possible to is take largely account due toof the residualoccurrence dependence of effective of sub-bandthe coefficients coders. between The EZW them. encoder More is precisely, the first tosince provide the high-energy remarkable coefficientsdistortion-rate are spatially performance grouped, while their allowing position progressive is effectively decoding comput ofed theby indicating image. The the principle position of thezerotrees, sets of orlow-energy other partitioning coefficients. structures After separation in sets of of zeros, the low-energy makes it possible coefficients to take and account the energetic of the coefficients,residual dependence the latter of is the relative coefficientsly independent between them.and can More be efficiently precisely, since coded the using high-energy quantification coefficients and entropyare spatially coding grouped, techniques. their position is effectively computed by indicating the position of the sets of low-energyIn 1996, coefficients. Said and Pearlman After separation proposed of a the hierarchical low-energy tree coefficients partitioning and coding the energetic scheme coefficients, called set partitioningthe latter is relativelyin hierarchical independent tree (SPIHT) and can[6], bebased efficiently on the codedsame basic using EZW, quantification and exhibiting and entropy better performancecoding techniques. than the original EZW. VideoIn 1996, compression Said and Pearlman algorithms proposed such
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