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IEEE COMMUNICATIONS LETTERS, VOL. 11, NO. 2, FEBRUARY 2007

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Image Error Concealment Using Watermarking with Subbands for Wireless Channels Gürkan Gür, Student Member, IEEE, Yücel Altu˘g, Emin Anarım, Member, IEEE, and Fatih Alagöz, Member, IEEE

Abstract— Transmission of block-coded images through errorprone wireless channels often results in lost blocks. In this study, we investigate a novel error concealment method for covering up these high packet losses and reconstructing a close approximation. Our scheme is a modified discrete wavelet transform (DWT) technique (namely, subbands based image error concealment (SIEC)) for embedding downsized replicas of original image into itself. We propose that this technique can be implemented for wireless channels to combat degradations in a backward-compatible scheme. We show that the proposed error concealment technique is promising, especially for the erroneous channels causing a wider range of packet losses, at the expense of computational burden. Index Terms— Error concealment, watermarking, error-prone channels.

I. I NTRODUCTION

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IRELESS channels may face severe adverse conditions, especially burst error conditions where errors are likely to occur in clusters. Error concealment (EC) techniques have been proposed to cope with signal degradations stemming from such conditions. The human eye can tolerate a certain degree of distortion in image and video signals, unlike the case in data transmission where lossless delivery is required absolutely. Thus, EC methods aim to obtain a close approximation of the original signal or attempt to make the output signal at the decoder least objectionable to human eyes [1]. Forward error correction (FEC), automatic repeat request (ARQ) and hybrid FEC/ARQ schemes may fail especially for the real-time applications subject to transmission over channels with high error rates and/or with high propagation delay [2]. Alternatively, EC techniques may leverage postprocessing rather than suffering the burden of retransmissions, consequent delay and an increase in the transmission bandwidth. For instance, consider a surveillance image transmitted through an error-prone satellite link: The data content may be extremely valuable and time-critical, and the receiver stations may be low-power, hand-held devices. In that case, the transmission efficiency can be improved via error concealment functionality. In previous work, we have proposed BWEC algorithm, an error concealment technique utilizing watermarking for embedding macroblock-based BNM (bestneighborhood-matching) information in order to utilize spatial

Manuscript received July 7, 2006. The associate editor coordinating the review of this letter and approving it for publication was Dr. Deepa Kundur. G. Gür and F. Alagöz are with the Department of Computer Engineering, Bo˘gaziçi University, Bebek 34342 Istanbul, Turkey (email: [email protected]). Y. Altu˘g and E. Anarım are with the Department of Electrical Engineering, Bo˘gaziçi University, Bebek 34342 Istanbul, Turkey. Digital Object Identifier 10.1109/LCOMM.2007.061055.

redundancy [3]. In this work, we propose another novel algorithm which is rather based on replica embedding and a different watermarking scheme to achieve better performance. This paper presents and evaluates our integrated error concealment technique, namely SIEC, utilizing watermarking with downsized replicas in wavelet domain. We propose that our technique can be implemented for wireless channels to combat image degradations in a backward-compatible scheme. II. E RROR C ONCEALMENT M ETHOD : SIEC DWT has advantages such as space-frequency localization, hierarchical multi-resolution presentation, superior HVS modeling and adaptivity [4]. It is often claimed that embedding watermarks in the transform domain such as wavelets is advantageous in terms of visibility and security [5]. We can classify watermarking mainly into two subgroups: fragile vs. robust. In fragile watermarking, the embedded watermark is used to detect the changes on the received signal. We have used this watermarking type for error detection in transmitted images. Conversely, in robust watermarking, embedded watermark is expected not to be altered in severe transmission conditions. We have embedded downsized replicas in wavelet domain using this latter type of watermarking. In SIEC algorithm, we embed the replicas of the original image’s M × M macroblocks in the subbands of the to-betransmitted image, excluding LL subbands, in order to limit the visual degradation. We have used two wavelet schemes: wavelet-tree [6] and pyramid-structured wavelet schemes [7]. The host macroblocks where replicas are embedded are chosen by using a shared-key-dependent pseudo-random sequence, so the extraction of the replicas are blind. If all of the replicas embedded in the subbands are lost, then each pixel in the lost macroblock is replaced by the median value of the sequence composed of non-zero values of neighboring macroblocks’ corresponding pixel. The proposed EC algorithm can be stated as follows: At the encoder, 1) Read the original image, I, with size of N1 × N2 pixels. 2) Check the image: if there are macroblocks consisting of all 0’s, then replace a pixel value in each of these macroblocks with 1. This step facilitates fragile watermarking for errordetection, and is inspired by work of Kundur et al [8]. 3) Take lth level pyramid-structured DWT of the original image I. Note that k ≥ l, where k is the number of levels of the tree structured DWT. 4) Store each (M/2k ) × (M/2k ) macroblock of the tree structured DWT of the original image, namely replicas. Note that there are (N1 × N2 /M 2 ) macroblocks.

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IEEE COMMUNICATIONS LETTERS, VOL. 11, NO. 2, FEBRUARY 2007

5) Scale each replica by the designated coefficient, then embed that scaled replica in each pyramid-structured wavelet subband, excluding LL ones, by using shared-key dependent sequence for each individual subband. Note that step 4 to 6 actualizes robust watermarking schema, which uses repeated watermark technique which is a modified version of the method studied by Kundur et al. [9]. 6) Take inverse DWT (IDWT) of the watermarked image, namely IW M , and round the floating-point pixel values to the corresponding integer values.

pgg = 1 - pgb

pbb = 1 - pbg pbg

good (ploss,g )

bad (ploss,b )

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Fig. 1. Gilbert channel model. Subscript b is for bad state, whereas g stands for good state and pij is the transition probability from state i to j where i, j ∈ {b, g}.

Then, the image is transmitted through the channel and macroblocks are lost according to the channel behavior. At the decoder, 1) Read the received image, Ireceived , and determine the lost blocks by searching macroblocks consisting of all 0’s. Thus, we utilize fragile watermarks in this step for error-detection. 2) Take lth level pyramid-structured DWT of the received image Ireceived . 3) By generating shared-key dependent random sequence, which was also used in the encoder, determine the location of lost macroblocks’ replicas for each individual subband. 4) Multiply each replica with the known scaling coefficient used in encoder and take k th level IDWT of the extracted replicas. 5) If there are more than one non-zero extracted macroblock, take average of all those non-zero macroblocks, then place that average into the received image, Ireceived , as the lost macroblock. After this process is finished, we have achieved extracted image, Iext . 6) Scan Iext for lost macroblocks, which could not be healed. If there are still macroblocks consisting of all 0’s, then replace them with the median value of the neighboring healthy macroblocks. This step improves SIEC’s performance by using a simple but auxilliary median method where our algorithm had failed to respond. After this process, we have constructed healed image Ihealed . III. C HANNEL M ODEL When data packets are transferred over channels with burst errors, packet error statistics are usually more important than bit error statistics to analyze the communication performance [10]. Therefore, error-prone channel in our work has been modeled as a two-state discrete-time Markov process in accordance with Gilbert model, as illustrated in Fig. 1. Packets belonging to an image are fully corrupted in the bad channel state (Ploss,b = 1) while packet losses in the good channel states are negligibly small (Ploss,g ≈ 0). We assume that the packet losses are in consecutive order based on the given transition probabilities. Even though this simplified model does not capture all the details of fading, it does provide a robust model for wireless channels with burst-error characteristics, such as satellite links. In the simulations, the transition state probabilities are adjusted in a way that desired levels of packet loss are achieved. Due to the fading dynamics of an errorprone channel and/or the bottleneck at the transmitters, this scenario may be highly expected in various applications.

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Fig. 2. (a) Original Lena, (b) watermarked Lena, (c) erroneous image received with 50% of macroblocks lost, (d) error-concealed image from (c).

IV. E XPERIMENTAL R ESULTS For the performance evaluation of our technique, we have conducted comprehensive sets of simulation experiments in MATLAB R14 environment. Peak-signal-to-noiseratio (PSNR) was employed as the performance metric. In our simulations, we have used grayscale “Lena”, “Baboon” and “Peppers” images of size 512×512 pixels. The macroblock lost during transmission had the size of 8×8 pixels in all cases. The base wavelet used for DWT was Haar wavelet. The channel parameters were set in a way that percentage of lost macroblocks during transmission ranged from 10% to 75%. As seen in the description of the algorithm, as the order of pyramid-structured DWT is increased, the chance of finding the embedded replica increases. On the other hand, the order of tree-structured DWT needs to increase as well, since it must be greater than or equal to the former. So the quality of the approximation for the lost macroblock, constructed from the extracted replicas, degrades compared to lower level approximations. This forms one design constraint for our algorithm. Another design constraint is the selection of the scaling coefficient. Briefly, we need to reasonably scale the pixel values of replicas, because they have considerable energy

¨ et al.: IMAGE ERROR CONCEALMENT USING WATERMARKING WITH SUBBANDS FOR WIRELESS CHANNELS GUR

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V. C ONCLUSION

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compared to the subbands into which they will be embedded. In addition, greater the scaling coefficient, greater signal-tonoise-ratio (SNR) of IW M versus I. On the other hand, if the scaling coefficient exceeds reasonable values, information can be lost during the rounding of the watermarked image. We choose 100 as the scaling coefficient, since it is large enough to make SNR large while rounding effect is kept below undesirable levels, as the results indicate. For visual evaluation, the original, watermarked and reconstructed “Lena” images with 2–level tree-structured/1–level pyramid-structured DWT for 50% packet loss are given in Fig. 2. SIEC technique seems to improve the perceptual quality of the distorted images substantially. The main contribution of our method is the relatively robust EC performance against high packet losses, usually encountered in mobile wireless channels such as land mobile satellite and cellular networks. Similarly, the numerical results for all three images, elucidated in Fig. 3, 4 and 5, show that as packet loss gets more severe, PSNR for the received image decreases as expected. The same trend is also valid for the reconstructed image. However, SIEC manages to keep an enhancement gap of 16.7 dB on average (min. 10 dB, max. 18.4 dB). For “Peppers” and “Baboon” images, the results are similar: an improvement of 15.7 dB on average (min. 10.1 dB, max. 17.3 dB) and 12.5 dB on average (min. 8.5 dB, max. 14.2 dB), respectively. “Baboon” image suffers some performance loss due to its texture-richness, and consequent less “wavelet-friendliness”, a phenomenon also observed in [3]. Nevertheless, in all cases, SIEC achieves satisfactory concealment performance at all distortion levels.

The presented results indicate that the proposed scheme is a promising image restoration technique which may enable image transmission systems to cope with burst-error channel conditions of wireless networks. Our scheme is backward compatible. Additionally, it is wavelet and block-processing based, which implies structural-compatibility with the current and upcoming image-video compression standards. Implementation of SIEC has provided substantial improvements in PSNR values of corrupt images. Clearly, this profound improvement comes at the expense of some computational burden and slight visual degradation. Currently, we are working to improve our algorithm to achieve better recovery results with less computational load and visual degradation. R EFERENCES [1] Y. Wang and Q.-F. Zhu, “Error control and concealment for video communication: a review,” Proc. IEEE, vol. 86, pp. 974–997, May 1998. [2] J. Zhu and S. Roy, “Performance of land mobile satellite communication (LMSC) channel with hybrid FEC/ARQ,” in Proc. GLOBECOM’02, pp. 2851–2854. [3] G. Gür, F. Alagöz, and M. Abdel-Hafez, “A novel error concealment method for images using watermarking in error-prone channels,” in Proc. 16th Annual IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC’05). [4] P. Meerwald and A. Uhl, “A survey of wavelet-domain watermarking algorithms,” in Proc. SPIE, Electronic Imaging, Security and Watermarking of Multimedia Contents III 2001, vol. 4314. [5] F. Hartung and M. Kutter, “Multimedia watermarking techniques,” Proc. IEEE, vol. 87, no. 7, pp. 1079–1107, July 1999. [6] J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Processing, vol. 41, no. 12, pp. 3445– 3462, 1993. [7] S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674–693, 1989. [8] D. Kundur and D. Hatzinakos, “Towards a telltale watermarking technique for tamper-proofing,” in Proc. IEEE Int. Conf. on Image Processing 1998, vol. 2, pp. 403–412. [9] ——, “Toward robust logo watermarking using multiresolution image fusion principles,” IEEE Trans. Multimedia, vol. 6, no. 1, pp. 185–198, Feb. 2004. [10] C. Jiao, L. Schwiebert, and B. Xu, “On modeling the packet error statistics in bursty channels,” in Proc. 27th Annual IEEE Conf. on Local Computer Networks (LCN’02), pp. 534–541.