Springer base paper

1 23

Signal, Image and Video Processing ISSN 1863-1703 SIViPDOI 10.1007/s11760-012-0325-1

Progressive medical image coding usingbinary wavelet transforms

Tirupathiraju Kanumuri, M. L. Dewal &R. S. Anand

1 23

Your article is protected by copyright and all

rights are held exclusively by Springer-Verlag

London Limited. This e-offprint is for personal

use only and shall not be self-archived in

electronic repositories. If you wish to self-

archive your work, please use the accepted

author’s version for posting to your own

website or your institution’s repository. You

may further deposit the accepted author’s

version on a funder’s repository at a funder’s

request, provided it is not made publicly

available until 12 months after publication.

SIViPDOI 10.1007/s11760-012-0325-1

ORIGINAL PAPER

Progressive medical image coding using binary wavelet transforms

Tirupathiraju Kanumuri · M. L. Dewal · R. S. Anand

Received: 25 September 2011 / Revised: 21 April 2012 / Accepted: 23 April 2012© Springer-Verlag London Limited 2012

Abstract In this paper, a new algorithm for progressivemedical image coding is presented. An 8-bit gray scaleimage is divided into eight binary bit-planes, and then, binarywavelet transform is performed on each bit-plane to extractthe three-level multi-resolution binary wavelet transformedimages. Starting from the most significant bit-plane, each bit-plane is encoded using quadtree-based partitioning scheme toexploit the energy concentration in the high-frequency sub-bands. Experiments are conducted on ultrasound, MRI andCT images to prove the effectiveness of the proposed algo-rithm. The results show a significant improvement in termsof bit-rate for the required peak signal-to-noise ratio and cor-relation coefficient as compared to the existing state-of-artprogressive image coding methods.

Keywords Binary wavelet transforms (BWT) · Progressiveimage coding · Medical image compression

1 Introduction

With the advent development of digital imaging and imageprocessing technology, all the hospitals are moving towarddigitization of medical images for processing, storage andtransmission purposes. This requires huge amount of stor-age space for data storage and higher band width for image

T. Kanumuri (B) · M. L. Dewal · R. S. AnandDepartment of Electrical Engineering, Indian Institute of Technology,Roorkee, Roorkee, 247667 Uttarakhand, Indiae-mail: [email protected]

M. L. Dewale-mail: [email protected]

R. S. Anande-mail: [email protected]

transmission. In telemedicine, the medical images are trans-mitted over long distances through Internet. This is mainlyused in remote places such as villages, ships and air planeswhere the specialized doctor is not available for diagnosis.As the communication channel in such places is very narrow,embedded image coding method that can provide progressivereconstruction is preferred so that the doctor can stop decod-ing based on the individual requirements at the decodingend. In this paper, a new method is proposed for progressivecoding of medical images. The previously available progres-sive image coding methods can be classified into three cat-egories: spatial domain methods [1–6], pyramidal structuremethods [7–10] and transform domain methods [11–27]. Outof the available techniques, transform domain techniques aremore efficient due to their compression efficiency and henceemployed in JPEG and JPEG 2000 image coding standards[11,16].

A progressive coding method based on prioritized codingof DCT coefficients is proposed in [11]. DCT suffers fromblocking artifacts for low bit-rates, and to avoid this, manywavelets-based embedded image coding methods are pro-posed. Shapiro [12] introduced lossy to lossless progressiveembedded image coding method, embedded zerotree wave-let coding (EZW). Further, Zandi et al. extended the EZWwith the reversible wavelets (CREW) [13]. Said and Pearl-man introduced an algorithm known as set partitioning inhierarchical trees (SPIHT) [14] which utilizes the conceptof parent–child relationship across wavelet subbands. Fur-ther, Pearlman and Asad have extended the SPIHT, set par-titioning embedded block coder (SPECK) [15] that exploitsthe parent–child relationship as well as clustering of energyin the frequency domain. Taubman proposed a block-basedcoding method, embedded block coding with optimized trun-cation of the embedded bit-streams (EBCOT) [16], and it isincluded in JPEG 2000. Pan and Siu introduced progressive

123

Author's personal copy

SIViP

partitioning binary wavelet tree coder (PPBWC) [17] usingbinary wavelet transforms (BWT). They have combined thefeatures of zigzag scanning in EZW [12] and partitioningpriority coding of progressive DCT [11] to encode the BWT.

A concise review of the available related literature, tar-geted for development of our algorithm, is presented. Binarywavelet transform (BWT) is first proposed for binary imagecompression [17–22]. Later, it has been extended to grayscale image by separating the gray scale image into series ofbinary bit-planes, and then, the BWT is performed on eachbit-plane [23]. Law and Sui proposed the in-place implemen-tation for BWT [17] using lifting scheme that is similar tothe lifting scheme in the real wavelet transform. BWT hasseveral advantages over the bi-orthogonal wavelets [25] andinteger wavelets [26], such as only basic Boolean operationsare involved, no quantization during transformation, no needto transmit the sign bit and the transformed image have thesame number of gray levels as the original image.

In BWT, the energy clusters in the transformed subbandscorrespond to the spatial locations associated with edges inthe original image. So, the energy is mainly concentratedin high-frequency subbands and the parent–child relation-ship is very weak [17]. So, the state-of-art progressive cod-ing methods SPIHT and SPECK are not efficient for binaryimage coding. In this paper, a new coding method is pro-posed, which uses the energy concentration property of BWTin high-frequency subband.

The paper is organized as follows: a concise review andimplementation of 1-D and 2-D BWT is discussed in Sect. 2.The Sect. 3 presents the proposed method for encoding 2-DBWT coefficients. The decoding process is given in Sect. 4.The experimental results in support of the proposed algo-rithm are discussed in Sect. 5. Finally, the proposed work isconcluded in Sect. 6.

2 Binary wavelet transform

2.1 1-D binary wavelet transform (1-D BWT)

The BWT is implemented on binary images in the similarmanner to the lifting scheme for real wavelet transforms ongray scale images [17].

Let x be an 1 × N signal, the transformed BWT coeffi-cients matrix W can be constructed as follows

W = [AD]T (1)

where

A = (a|s=0, a|s=2, . . . , a|s=N−2)T

(2)D = (d|s=0, d|s=2, . . . , d|s=N−2)

T

a|s=k defines a vector with elements formed from a circularshifted sequence of a by k. and

a = {a0, a1, . . . , aS−1}T

(3)d = {d0, d1, . . . , dS−1}T

where S is the number of scales, ai and di are the approximate(lowpass) and detail (highpass) coefficients, respectively.

The BWT is then defined [23] as:

y = W x (4)

Law and Sui proposed the in-place implementation for BWT[17]. The 32 length-8 binary filters are classified into fourgroups depending on the number of ‘1’s in the binary fil-ters. Examples of the binary filters in each group are givenin Table 1. In the proposed method, filters of group 1 areused because the entropy of the transformed image is lesscompared to other filters and hence more suitable for imagecompression.

In order to have an in-place implementation structure, theodd number and even number samples of the original signalare split into two sequences. These two sequences are thenupdated according to the filter coefficients from the low passand high pass filters. The low pass and the high pass outputsare then interleaved together to get the transformed output.The scheme is depicted in Fig. 1. If length of the input signalis odd, the last sample is separated out and BWT is appliedfor remaining signal and the last sample is added at the endof low pass output. For example, if the input signal is of15 bits length, the low pass filter will have 8 samples andhigh pass filter will have 7 samples. To go for the next leveldecomposition, BWT is applied to the lowpass output.

2.2 2-D binary wavelet transform (2-D BWT)

A separable 2-D BWT [28] can be computed efficiently inbinary space by applying the associated one-level 1-D fil-ter bank to each row of the input binary image and to eachcolumn of the resultant low pass and high pass output coef-ficients as shown in Fig. 2. This can be extended to grayscale image by separating it into binary bit-planes, and thenperforming the BWT to each individual bit-plane of imageas shown in Fig. 2. To go for second-level decomposition,the 2-D BWT is applied to the LL subband.

Table 1 Length-8 binary wavelet filters

Group Lowpass filter Highpass filter

1 {1, 0, 0, 0, 0, 0, 0, 0} {1, 1, 0, 0, 0, 0, 0, 0}

2 {1, 1, 1, 0, 0, 0, 0, 0} {1, 1, 0, 0, 0, 0, 0, 0}

3 {1, 1, 1, 1, 0, 0, 0, 1} {1, 1, 0, 0, 0, 0, 0, 0}

4 {1, 1, 1, 1, 1, 1, 1, 0} {1, 1, 0, 0, 0, 0, 0, 0}

123


SIViP

3 The proposed encoder

In real and integer wavelet transforms, the energy is concen-trated in low-frequency subbands and a parent–child relation-ship exists. So, SPIHT [14] starts from the low-frequency

Fig. 1 In-place implementation of 1-D BWT for one level

subbands by considering them as parents and then go forthe high-frequency subbands. SPECK [15] uses block-basedcoding by checking low-frequency subbands first, and then,it checks the high-frequency subbands. The energy clustersin the binary transform subbands correspond to the spatiallocations associated with edges in the original image. So, theenergy is mainly concentrated in high-frequency subbands.In the proposed method, higher priority is given for encodinghigh-frequency subbands than the low-frequency subbands.In PPBWC [17], each pixel is checked for significance inevery loop until it becomes significant. In case of ultrasoundimages, most of the pixels have very low values, and thus, itnecessitates transmitting more bits for checking the signifi-cance alone. Thus, it is not efficient. In the proposed method,block-based coding is used, which requires only 4 bits tobe transmitted if the entire bit-plane is zero. In the proposedmethod, more emphasis is given for checking high-frequencysubbands to exploit energy concentration.

The flow chart of the proposed method is given in Fig 3.The input gray scale image is first decomposed into binarybit-planes, and then, three-level 2-D BWT is calculated foreach bit-plane starting from the most significant bit-plane(MSB) to the least significant bit-plane (LSB). Initially, thebinary wavelet transformed image of each bit-plane is dividedinto 4 blocks as shown in Fig. 4. To give higher priority for

Fig. 2 One-level 2-D BWT implementation for one bit-plane of gray scale image

123


SIViP

Fig. 3 Flow chart of theproposed encoder

Fig. 4 Quadtree partitioning oftransformed binary image

encoding high-frequency subbands, HH1 is taken as B1, LH1is taken as B2, HL1 is taken as B3, and the remaining blockis taken as B4.

The significance of the block B is checked by

τn(B) ={

1, i f max(i, j)∈B

{|ci, j |} = 1

0, else(5)

where ci, j is transformed coefficient at pixel location (i, j)B1, B2, B3 and B4 are checked for significance in serial

order using Eq. (5). If Bi (i = 1 : 4) is not significant, 0is added to output and further processing of Bi is discarded,and it goes for the next block. If it is significant, 1 is addedto output to indicate to the decoder that it is significant andit is decomposed into four equal parts as shown in Fig. 5,

and each of the blocks is checked for significance. As eachtime 4 blocks are checked for significance, each 4 bits of theoutput are combined together and converted to the decimalform. After completely processing each bit-plane, output isencoded using Huffman coding. Now, it moves on to the nextbit-plane and undergoes the same process. As the proposedmethod involves only simple checking and division opera-tions, it is easy to implement.

4 Decoding process

The data are received from MSB bit-plane to LSB bit-plane.User can stop decoding after any bit-plane when the required

123


SIViP

Fig. 5 Quadtree partitioning of B

clarity is reached. The flow chart for the decoder of theproposed method is shown in Fig. 6. The flow chart beingself-explanatory, the procedure is explained briefly. For eachloop (bit-plane), apply Huffman decoding and then converteach decimal value to 4 bit binary and keep in variable OUT.Initialize O to 1. Divide the current bit-plane of image into4 blocks B1, B2, B3, and B4 that is, we keep the startingpixel location and size of each block to locate the corre-

Table 2 Length-8 inverse binary wavelet filters

Group Lowpass filter Highpass filter

1 {1, 1, 0, 0, 0, 0, 0, 0} {0, 1, 0, 0, 0, 0, 0, 0}

2 {0, 0, 1, 1, 0, 0, 0, 0} {0, 1, 1, 1, 0, 0, 0, 0}

3 {0, 0, 0, 0, 0,0, 1, 1} {1, 0, 0, 0, 1, 1, 1, 1}

4 {0, 0, 0, 0, 0,0, 1, 1} {0, 1, 1, 1, 1, 1, 1, 1}

sponding block in the original image. For example, if theimage size is 512 × 512, we keep B1 = (257, 257, 256),B2 = (257, 1, 256), B3 = (1, 257, 256), B4 = (1, 1, 256).If the value of OUT(O) is 0, keep all the pixels of block aszeros, increment the value of O by one and go to next block.

Else, divide the block into 4 blocks, increment the value ofO by one and check each block for significance as shown inprocess B(). For example, if we are processing B1, the newblocks will be B1 = (129, 129, 128), B2 = (129, 1, 128),

Fig. 6 flow chart for thedecoder of the proposed method

123


SIViP

Fig. 7 The output of EBCOTmethod after each loop forultrasound image

Fig. 8 The output of SPIHTmethod after each loop forultrasound image

123


SIViP

Fig. 9 The output of SPECKmethod after each loop forultrasound image

Fig. 10 The output of PPBWCmethod after each loop forultrasound image

123


SIViP

Fig. 11 The output of theproposed method after each loopfor ultrasound image

Table 3 Comparative results of PSNR versus bit-rate for ultrasound image

Result after loop EBCOT SPIHT SPECK PPBWC PROPOSED

PSNR (dB) Bit-rate PSNR (dB) Bit-rate PSNR (dB) Bit-rate PSNR (dB) Bit-rate PSNR (dB) Bit-rate

1 27 0.0465 27 0.0313 27 0.0313 27 0.03636 27 0.0313

2 28 0.0720 28 0.0571 28 0.057 29 0.1194 28 0.0792

3 28.5 0.125 28.5 0.1212 28.5 0.1066 31 0.2 32 0.1212

4 30 0.21 30 0.2 30 0.2 38 0.2758 38 0.1632

5 35 0.32 35 0.3076 35 0.32 46 0.32 45 0.2051

6 42 0.42 42 0.4210 42 0.4 55 0.3636 54 0.25

7 48 0.53 48 0.533 48 0.5 64 0.3809 Inf 0.2962

8 Inf 0.666 Inf 0.6153 Inf 0.6153 Inf 0.3892

B3 = (1, 129, 128), B4 = (1, 1, 128). While processing ablock if it is reached to single pixel and value of OUT(O)is 1, then replace that pixel value in the current bit-plane ofthe image with 1. After completing the decoding of the entirebit-plane, apply three-level inverse BWT and combine all thebit-planes to form gray scale image. Examples of the inversebinary filters in each group are given in Table 2. Now checkwhether the required clarity is reached. If it has not reached,go for the next loop.

5 Experimental results and discussions

The performance of the proposed method is evaluated usingthe bit-rate for the given peak signal-to-noise ratio (PSNR)and the correlation coefficient (CoC) [27]. A set of ten MRI,ten CT images and five ultrasound images are taken for ourexperiment. The proposed method is compared with the state-of-art progressive image coders. SPIHT [14], SPECK [15]and EBCOT [16] are implemented using (2, 2) integer wave-

123


SIViP

Fig. 12 Plot for PSNR versus bit-rate for ultrasound image

let transforms, and the PPBWC [17] is implemented usingBWT. We got almost similar results for all the images of eachset, and hence, the results for one image from each group arepresented in this section.

For ultrasound images, most of the pixels’ gray levels arenear to zero. Hence, most of the bit-planes consist of onlyzeros, and they need not be encoded. In the proposed method,only four bits are transmitted if all the pixels in the bit-planeare zero. But, in PPBWC and SPIHT, more bits are to betransmitted. Thus, the proposed method requires lesser bit-rate for the required PSNR and CoC. The outputs after eachloop for each method are shown in Figs. 7, 8, 9, 10 and 11.In each figure, the first image shows the input and secondimage onward shows the output after each loop. The com-parative results of the bit-rate versus PSNR after each loopfor one image are shown in Table 3, and the correspondingplots are shown in Fig. 12. The results for bit-rate versus CoCafter each loop are shown in Table 4, and the correspondingplots are shown in Fig. 13. From the plot, it can be seen thatup to 2nd loop, the proposed method is giving comparableresults and from the 3rd loop onwards, requires very lessbit-rate when compared with bit-rates obtainable from othermethods for the required PSNR and CoC. The user can stop

Fig. 13 Plot for bit-rate versus CoC for ultrasound image

decoding after any loop depending on the clarity required formaking the diagnosis. For lossless reconstruction, the bit-raterequired for the proposed method is only 0.2962 where as itis 0.3892 for PPBWC and is above 0.6 for the other methods.From the results and above observations, it is cleared that theproposed method outperforms all the methods on ultrasoundimages.

For MRI images, most of the pixels’ gray levels havemedium values. Hence, the proposed method needs morebits initially giving inferior results for low bit-rate trans-mission. But, for medium and high bit-rates, the proposedmethod gives better results than existing methods. The out-puts after each loop for each method are shown in Figs. 14,15, 16, 17 and 18. In each figure, the first image shows theinput and second image onward shows the output after eachloop. The comparative results of the bit-rate versus PSNRare shown in Table 5, and the corresponding plots are shownin Fig. 19. The results for bit-rate versus CoC are shown inTable 6, and the corresponding plots are shown in Fig. 20.From the plots, it can be seen that for a PSNR of 33 andCoC of 0.96, the PPBWC is giving better results than theproposed method and for PSNR of above 33 and CoC ofabove 0.96, the proposed method is giving better results

Table 4 Comparative results of CoC versus bit-rate for ultrasound image


CoC Bit-rate CoC Bit-rate CoC Bit-rate CoC Bit-rate CoC Bit-rate

1 0.3496 0.0465 0.3496 0.3137 0.349 0.0313 0.5797 0.03636 0.5723 0.3137

2 0.7968 0.0720 0.7968 0.0571 0.7968 0.057 0.8862 0.1194 0.9711 0.0792

3 0.9366 0.125 0.9366 0.1212 0.9366 0.1066 0.9723 0.2 0.9931 0.1212

4 0.9820 0.21 0.9825 0.2 0.9825 0.2 0.9944 0.2758 0.9985 0.1632

5 0.9956 0.32 0.99 0.3076 0.9956 0.32 0.998 0.32 0.999 0.2051

6 0.998 0.42 0.9989 0.4210 0.9989 0.4 0.9998 0.3636 0.9999 0.25

7 0.999 0.53 0.999 0.533 0.999 0.5 0.9999 0.3809 1 0.2962

8 1 0.666 1 0.6153 1 0.6153 1 0.3892

123


SIViP

Fig. 14 The output of theEBCOT method after each loopfor MRI image

Fig. 15 The output of theSPIHT method after each loopfor MRI image

123


SIViP

Fig. 16 The output of theSPECK method after each loopfor MRI image

Fig. 17 The output of thePPBWC method after each loopfor MRI image

123


SIViP

Fig. 18 The output of theproposed method after each loopfor MRI image

Table 5 Comparative results of PSNR versus bit-rate for MRI image



1 28 0.0465 28 0.037 28 0.0367 29 0.03921 28 0.0313

2 29 0.0683 29 0.0529 29 0.0479 33 0.09638 29 0.0321

3 30 0.1176 30 0.1095 30 0.0898 34 0.16 30 0.0588

4 31 0.186 31 0.1702 31 0.1568 37 0.2162 32 0.0879

5 35 0.258 35 0.25 35 0.2424 40 0.2758 37 0.123

6 42 0.3478 42 0.333 42 0.3333 50 0.333 44 0.170

7 48 0.47 48 0.444 48 0.421 59 0.3636 52 0.222

8 Inf 0.6153 Inf 0.5714 Inf 0.5333 Inf 0.4 Inf 0.266

than the existing methods. For lossless reconstruction, thebit-rate is only 0.266 for the proposed method where as itis 0.4 for PPBWC, above 0.5 for other methods. From theresults, it is clear that the proposed method outperformsall the methods for medium and high bit-rates for MRIimages.

For CT images, all type of gray values exists. The outputsafter each loop for each method are shown in Figs. 21, 22, 23,24 and 25. In each figure, the first image shows the input andsecond image onward shows the output after each loop. Thecomparative results of the bit-rate versus PSNR are shownin Table 7, and the corresponding plots are shown in Fig. 26.The results for bit-rate versus CoC are shown in Table 8, and

Fig. 19 Plot for PSNR versus bit-rate for MRI image

123


SIViP

Table 6 Comparative results of CoC versus bit-rate for MRI image



1 0.6627 0.0465 0.6627 0.037 0.6627 0.0367 0.7304 0.03921 0.7079 0.0313

2 0.9166 0.0683 0.9167 0.0529 0.9167 0.0479 0.967 0.09638 0.9683 0.0321

3 0.9751 0.1176 0.9751 0.1095 0.9751 0.0898 0.99 0.16 0.9896 0.0588

4 0.9927 0.186 0.9927 0.1702 0.9927 0.1568 0.9975 0.2162 0.9972 0.0879

5 0.9979 0.258 0.9979 0.25 0.9979 0.2424 0.9988 0.2758 0.9989 0.123

6 0.999 0.3478 0.9993 0.333 0.9994 0.3333 0.9997 0.333 0.9997 0.170

7 0.9998 0.47 0.9998 0.444 0.9998 0.421 0.9999 0.3636 0.9999 0.222

8 1 0.6153 1 0.5714 1 0.5333 1 0.4 1 0.266

Fig. 20 Plot for bit-rate versus CoC for MRI image

the corresponding plots are shown in Fig. 27. The results arealmost nearer to those obtained using PPBWC method forlow and medium PSNR values. For a PSNR of above 30 andCoC of above 0.69, the proposed method is performing betterthan all other methods. For lossless reconstruction, the bit-rate required is only 0.1702 for the proposed method whereas it is 0.266 for PPBWC and above 0.4 for the other meth-ods. From the results, it can be seen that the proposed methodoutperforms all the methods for CT images as well.

Fig. 21 The output of theEBCOT method after each loopfor CT image

123


SIViP

Fig. 22 The output of theSPIHT method after each loopfor CT image

Fig. 23 The output of theSPECK method after each loopfor CT image

123


SIViP

Fig. 24 The output of thePPBWC method after each loopfor CT image

Fig. 25 The output of theEBCOT method after each loopfor CT image

123


SIViP

Table 7 Comparative results of PSNR versus bit-rate for CT image



1 29 0.0597 29 0.0487 29 0.0418 30 0.05263 29 0.0313

2 29.5 0.0963 29.5 0.086 29.5 0.0808 38 0.09195 29.5 0.0329

3 30 0.1428 30 0.1269 30 0.123 40 0.125 30 0.0473

4 33 0.1904 33 0.16 33 0.16 43 0.1568 33 0.0666

5 38 0.25 38 0.2162 38 0.2162 49 0.1904 40 0.09

6 45 0.32 45 0.2857 45 0.2962 55 0.2222 47 0.1159

7 51 0.4 51 0.3636 51 0.3333 63 0.25 55 0.1428

8 Inf 0.47 Inf 0.4444 Inf 0.4 Inf 0.2666 Inf 0.1702

Fig. 26 Plot for PSNR versus bit-rate for CT image

6 Conclusions

In this paper, quadtree-based image coding method isproposed suitable for medical image coding. It utilizesthe energy concentration property of binary wavelet trans-forms in high-frequency subbands. The effectiveness of the

Fig. 27 Plot for CoC versus bit-rate for CT image

proposed method is well established through the resultsobtained on ultrasound image of size 640 × 480, MRI andCT images of size 512×512. From the experimental results,it is clear that the proposed method outperforms for all thebit-rates on ultrasound and CT images and for bit-rates ofabove 0.07 on MRI images.

Table 8 Comparative results of CoC versus bit-rate for CT image



1 0.7668 0.0597 0.7668 0.0487 0.7668 0.0418 0.676 0.05263 0.6908 0.0313

2 0.9616 0.0963 0.9616 0.086 0.9616 0.0808 0.9919 0.09195 0.9676 0.0329

3 0.9914 0.1428 0.9914 0.1269 0.9914 0.123 0.998 0.125 0.9953 0.0473

4 0.9975 0.1904 0.9975 0.16 0.9975 0.16 0.9994 0.1568 0.9991 0.0666

5 0.9992 0.25 0.9992 0.2162 0.9992 0.2162 0.9998 0.1904 0.9998 0.09

6 0.9997 0.32 0.9997 0.2857 0.9997 0.2962 0.9999 0.2222 0.9999 0.1159

7 0.9999 0.4 0.9999 0.3636 0.9999 0.3333 0.9999 0.25 0.9999 0.1428

8 1 0.47 1 0.4444 1 0.4 1 0.2666 1 0.1702

123


SIViP

References

1. Chang, C.C., Shine, F.C., Chen, T.S.: A new scheme of progressiveimage transmission based on bit-plane method. In: Proceedings ofFifth Asian Pacific Conference on Communications and FourthOptoelectronics and Communications Conference, Beijing, China,pp. 892–895 (1999)

2. Jiang, J.H., Chang, C.C., Chen, T.S.: Selective progressive imagetransmission using diagonal sampling technique. In: Proceedingsof International Symposium on Digital Media Information Base,Nara, Japan, pp. 56–67 (1997)

3. Chang, C.C., Ja, J., Chen, T.S.: A fast reconstruction method fortransmitting images progressively. Proc. IEEE. Trans. ConsumerElectron. 44(4), 1225–1233 (1998)

4. Chung, K.L., Tseng, S.Y.: New progressive image transmissionbased on quadtree and shading approach with resolution control.Proc. J. Pattern Recognit. 22, 1545–1555

5. Hung, K.L., Chang, C.C.: New irregular sampling coding methodfor transmitting images progressively. Proc. IEEE Vis. Image Sig-nal Process. 105(1), 44–50 (2003)

6. Yu-Chen, H., Ji-Han, J.: Low complexity progressive image trans-mission scheme based on quadtree tree segmentation. Proc. J. RealTime Imaging 11, 59–70 (2005)

7. Wang, L., Goldberg, M.: Progressive image transmission using vec-tor quantization on images in pyramid form. Proc. IEEE Trans.Commun. 37(12), 1341–1348 (1989)

8. Goldberg, M., Wang, L.: Comparative performance of pyramid datastructures for progressive image transmission. Proc. IEEE Trans.Commun. 39(4), 540–548 (1991)

9. Aiazzi, B., Alparone, L., Baronti, S.: A reduced Laplacian pyramidfor lossless and progressive image transmission. Proc. IEEE Trans.Commun. 44(1), 18–22 (1996)

10. Qiu, G.: A progressively predictive image pyramid for efficientlossless coding. Proc. IEEE Trans. Image Process. 8(1), 109–115 (1999)

11. Huang, Y., Driezen, H.M., Galatsanos, N.P.: Prioritized DCT forcompression and progressive transmission of images. Proc. IEEETrans. Image Process. 1(4), 477–487 (1992)

12. Shapiro, J.M.: Embedded image coding using zerotrees of wave-let coefficients. Proc. IEEE Trans. Image Process. 41(12), 3445–3462 (1993)

13. Zandi, A., Allen, J.D., Schwartz, E.L., Boliek, M.: CREW: com-pression with reversible embedded wavelets. In: Proceedings ofIEEE Data Computer Conference, pp. 212–221 (1995)

14. Said, A., Pearlman, W.A.: A new fast and efficient image codecbased on set partitioning in hierarchical trees. Proc. IEEE Trans.Circuits Syst. Video Technol. 6(3), 243–250 (1996)

15. Pearlman, W.A., Islam, A., Nagaraj, N., Said, A.: Efficientlow-complexity image coding with a set-partitioning embed-ded block coder. Proc. IEEE Trans. Circuits Syst. Video Tech-nol. 14(11), 1219–1235 (2004)

16. Taubman, D.: High performance scalable image compression withEBCOT. Proc. IEEE Trans. Image Process. 9(7), 1159–1170 (2000)

17. Pan, H., Siu, W.C., Law, N.F.: Lossless image compression employ-ing binary wavelet transforms. Proc. IET Image Process. 1(4), 353–362 (2007)

18. Swanson, M.D., Tewfik, A.H.: A binary wavelet decompositionof binary images. Proc. IEEE Trans. Image Process. 5, 1637–1650 (1996)

19. Kamstra, L.: The design of linear binary wavelet transforms andtheir application to binary image compression. In: Proceedingsof IEEE International Conference Image Processing, ICIP’03, pp.241–244 (2003)

20. Kamstra, L.: Nonlinear binary wavelet transforms and their appli-cation to binary image compression. In: Proceedings of 2003 IEEEInternational Conference Image Processing, pp. 593–596 (2002)

21. Gerek, Ö.N., Çetin, A.E., Tewfik, A.H.: Subband coding of binarytextual images for document retrieval. In: Proceedings of IEEEInternational Conference Image Processing, ICIP ’96, pp. 899–902(1996)

22. Pan, H., Jin, L.Z., Yuan, X.H., Xia, X.Y., Xia, L.Z.: Contextbased embedded image compression using binary wavelet trans-form. Proc. J. Image Vis. Comput. 28, 991–1002 (2010)

23. Law, N.F., Siu, W.C.: A filter design strategy for binary field wave-let transform using the perpendicular constraint. Proc. J. SignalProcess. 87(11), 2850–2858 (2007)

24. Sweldens, W.: The lifting scheme: a construction of second gener-ation wavelets. Proc. SIAM J. Math. Anal. 29(2), 511–546 (1997)

25. Adams, M.D., Kossentini, F.: Reversible integer-to-integer wave-let transform for image compression: performance evaluation andanalysis. Proc. IEEE Trans. Image Process. 8(6), 1010–1024 (2000)

26. Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Imagecoding using wavelet transforms. Proc. IEEE Trans. Image Pro-cess. 1, 205–220 (1992)

27. Kanumuri, T., Dewal, M.L., Anand, R.S.: Lossy to lossless medicalimage coding using joint bit scanning method. Proc. Comput. Eng.Intell. Syst. 2(4), 101–109 (2011)

28. Murala, S, Maheshwari, R.P., Balasubramanian, R.: Directionalbinary wavelet patterns for biomedical image indexing andretrieval. Proc. J. Med. Syst. doi:10.1007/s10916-011-9764-4

123


http://dx.doi.org/10.1007/s10916-011-9764-4

Springer base paper

Documents

Transcript of Springer base paper