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		<title><![CDATA[ Circuits and Systems for Video Technology, IEEE Transactions on - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 76 </description>
		<year>2012</year>
		<month>February </month>
		<day>10</day>
		<item>
			<title><![CDATA[Table of contents]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145224]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145224]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>C1</startPage>
			<endPage>C1</endPage>
			<fileSize>228</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[IEEE Transactions on Circuits and Systems for Video Technology publication information]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145225]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145225]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>C2</startPage>
			<endPage>C2</endPage>
			<fileSize>41</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[A Full Reference Quality Metric for Compressed Video Based on Mean Squared Error and Video Content]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5783335]]></link>
			<description><![CDATA[Visual quality of compressed video sequences depends on factors including spatial texture content and cognition-based factors such as prior knowledge and task in hand. The MOSp metric is a full reference objective quality metric which predicts perceived quality of sequences with video compression-induced impairments based on the spatial texture content and the mean squared error between original and compressed video sequences. In this paper, we extend the MOSp metric to incorporate cognition-based factors to identify regions in a video scene that attract human attention. The proposed metric has been tested on a variety of multimedia sequences of common intermediate format resolution compressed at a wide range of bitrates using the H.264/AVC coding standard. This metric shows a higher correlation with mean opinion score (MOS) than popular metrics, such as peak signal-to noise ratio, National Telecommunications and Information Administration/Institute for Telecommunication Sciences video quality metric, PSNRplus, and the Yonsei University metric. Results also show that by extending the MOSp metric to incorporate cognition-based factors such as skin information, its correlation with subjective scores (MOS) can be significantly improved in video content where humans are present. This algorithm is particularly useful for real-time quality estimation of multimedia sequences with block-based video compression-induced impairments because all the parameters of the metric can be calculated automatically with a modest amount of processing overhead.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5783335]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>165</startPage>
			<endPage>173</endPage>
			<fileSize>4105</fileSize>
			<authors><![CDATA[Bhat, A.;Kannangara, S.;Zhao, Y.;Richardson, I.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Source Modeling for Distributed Video Coding]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873137]]></link>
			<description><![CDATA[This paper studies source and correlation models for distributed video coding (DVC). It first considers a two-state HMM, i.e., a Gilbert&#x2013;Elliott process, to model the bit-planes produced by DVC schemes. A statistical analysis shows that this model allows us to accurately capture the memory present in the video bit-planes. The achievable rate bounds are derived for these ergodic sources, first assuming an additive binary symmetric correlation channel between the two sources. These bounds show that a rate gain can be achieved by exploiting the sources memory with the additive BSC model. A Slepian&#x2013;Wolf decoding algorithm which jointly estimates the sources and the source model parameters is then described. Simulation results show that the additive correlation model does not always fit well with the correlation between the actual video bit-planes. This has led us to consider a second correlation model (the predictive model). The rate bounds are then derived for the predictive correlation model in the case of memory sources, showing that exploiting the source memory does not bring any rate gain and that the noise statistic is a sufficient statistic for the MAP decoder. We also evaluate the rate loss when the correlation model assumed by the decoder is not matched to the true one. An a posteriori estimation of the correlation channel has hence been added to the decoder in order to use the most appropriate correlation model for each bit-plane. The new decoding algorithm has been integrated in a DVC decoder, leading to a rate saving of up to 10.14% for the same PSNR, with respect to the case where the bit-planes are assumed to be memoryless uniform sources correlated with the SI via an additive channel model.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873137]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>174</startPage>
			<endPage>187</endPage>
			<fileSize>9499</fileSize>
			<authors><![CDATA[Toto-Zarasoa, V.;Roumy, A.;Guillemot, C.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Decomposition of Dynamic Textures Using Morphological Component Analysis]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873134]]></link>
			<description><![CDATA[The research context of this paper is dynamic texture analysis and characterization. Many dynamic textures can be modeled as large scale propagating wavefronts and local oscillating phenomena. After introducing a formal model for dynamic textures, the morphological component analysis (MCA) approach with a well-chosen dictionary is used to retrieve the components of dynamic textures. We define two new strategies for adaptive thresholding in the MCA framework, which greatly reduce the computation time when applied on videos. Tests on real image sequences illustrate the efficiency of the proposed method. An application to global motion estimation is proposed and future prospects are finally exposed.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873134]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>188</startPage>
			<endPage>201</endPage>
			<fileSize>22044</fileSize>
			<authors><![CDATA[Dubois, S.;Peteri, R.;Menard, M.;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Mixed Layer Multiple Description Video Coding Scheme]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873135]]></link>
			<description><![CDATA[Multiple description coding (MDC) is a technique where multiple streams from a source video are generated, each individually decodable and mutually refinable. MDC is a promising solution to overcome packet loss in video transmission over noisy channels, particularly for real-time applications in which retransmission of lost information is not practical. A problem with conventional MDC is that the achieved side distortion quality is considerably lower than single description coding (SDC) quality except at high redundancies which in turn leads to central quality degradation. In this paper, a new mixed layer MDC scheme is presented with no degradation in central quality, and providing better side quality (approximately as much as that of SDC) compared to conventional methods. Also, this property directly leads to higher average quality when delivering the video in lossy networks. For each discrete cosine transform coefficient, we generate two coefficients: base coefficient (BC) and enhancement coefficient which are combined together. When all descriptions are available, they are decomposed and decoded to achieve high quality video. When one description is not available, we use estimation to extract as much of the BC as possible from the received description. Simulation results show that the proposed scheme leads to an improved redundancy-rate-distortion performance compared to conventional methods. The algorithm is implemented in JM16.0 and its performance for two-description and four-description coding is verified by experiments.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873135]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>202</startPage>
			<endPage>215</endPage>
			<fileSize>12712</fileSize>
			<authors><![CDATA[Kazemi, M.;Sadeghi, K. H.;Shirmohammadi, S.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Generation of an Optimum Patrol Course for Mobile Surveillance Camera]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873136]]></link>
			<description><![CDATA[Video surveillance systems are becoming increasingly important for crime investigation and deterrence, and the number of cameras installed in public space is increasing. However, many cameras installed at fixed positions are required to observe a wide and complex area. In order to efficiently observe such a wide area at lower cost, mobile robots are an attractive option. In this paper, we propose a method for determining the traveling route of a mobile surveillance camera. Our method is based on mixed integer linear programming and obtains an optimum traveling route such that a camera with a certain visual angle and visual distance can observe the entire region at the shortest intervals. Through our experiments, we apply this method to several artificially generated data and data for a real university campus and demonstrate that effective patrol courses for specified mobile surveillance cameras can be generated.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5873136]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>216</startPage>
			<endPage>224</endPage>
			<fileSize>1834</fileSize>
			<authors><![CDATA[Tomioka, Y.;Takara, A.;Kitazawa, H.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Advanced Adaptation Techniques for Improved Video Perception]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5929544]]></link>
			<description><![CDATA[Three different advanced adaptation techniques for improving the video perception of users are proposed in this paper. The proposed techniques exploit different adaptation decision-taking and adaptation approaches to adapt particular core parameters while considering diverse contextual information and constraints to achieve improved video perception of users. The first proposed technique employs a utility-based adaptation approach to perform adaptation operations on spatial resolution, frame rate, and quality scalability parameters according to the content-related contextual information (i.e., motion activity and structural feature) while fulfilling network bandwidth and terminal display size constraints. Using this technique, video contents can be adapted with the scalability parameters best fitting users' and contextual constraints' needs to achieve improved video perception. The second technique relies on prioritizing key frame, non-key frame, and temporal layer parameter-related network abstraction layer units to adapt video contents to satisfy network bandwidth constraint. The rate-distortion performances of adapted video contents can be improved by utilizing this technique in adaptation operations both in terms of bit rate of adapted video contents and video perception of users. The third technique is based on adapting the bit rate of 3-D video contents according to the changes in ambient illumination of the viewing environment. The adaptation results evaluated by either subjective or objective quality assessment techniques prove that all of the proposed techniques are efficient to improve the video perception of users.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5929544]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>225</startPage>
			<endPage>240</endPage>
			<fileSize>9215</fileSize>
			<authors><![CDATA[Nur, G.;Arachchi, H. K.;Dogan, S.;Kondoz, A. M.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Block Adaptive Interpolation Filter Using Trained Dictionary for Sub-Pixel Motion Compensation]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5929545]]></link>
			<description><![CDATA[Adaptive interpolation filtering for sub-pel motion compensation is one of key techniques of ITU-T key technology area (KTA) codec. However, the adaptive interpolation filtering has a limitation in coding efficiency because of its frame-based update strategy of filter coefficients. Although switched interpolation filter with offset is presented as a sort of block-adaptive filtering for KTA codec, its coding efficiency is generally lower than that of the best adaptive interpolation filter. In order to overcome such a problem, this paper presents an advanced block-adaptive interpolation filtering using well-trained dictionaries which store optimized filter coefficients. We derive those filter coefficients by using learning-based super-resolution. The proposed block-adaptive interpolation filtering for quarter-pel motion compensation consists of two steps: up-scaling of half-pel accuracy and subsequent up-scaling of quarter-pel accuracy. The dictionary optimized for each step is employed to produce the precise up-scaled pixels. Simulation results show that the proposed algorithm improves higher coding efficiency than the previous adaptive interpolation filters for KTA.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5929545]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>241</startPage>
			<endPage>248</endPage>
			<fileSize>4353</fileSize>
			<authors><![CDATA[Cho, J.;Jeong, S.-C.;Lee, D.-B.;Song, B. C.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Zero-Quantized Inter DCT Coefficient Prediction for Real-Time Video Coding]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5934378]]></link>
			<description><![CDATA[Several algorithms were proposed to predict the zero-quantized DCT coefficients and reduce the computational complexity of transform and quantization. It is observed that these prediction algorithms achieve good performance for all-zero-quantized DCT blocks. However, the efficiency is much lower for non-all-zero-quantized DCT blocks. This paper proposes an algorithm to improve the prediction efficiency for non-all-zero-quantized DCT blocks. The proposed method extends the prediction to 1-D transforms by developing new Gaussian distribution based thresholds for 1-D transformation. Moreover, the proposed algorithm can perform the prediction on 1-D transforms in both the pixel domain and the transform domain. The prediction for the first stage of 1-D transforms is performed in the pixel domain. However, the second stage of 1-D transforms is performed in the 1-D DCT domain. Because after the first stage of 1-D transforms most energy is concentrated to a few low frequency 1-D DCT coefficients, many transforms in the second stage are skipped. Furthermore, the method fits well the traditional row and column transform structure, and it is more implementation friendly. Simulation results show that the proposed model reduces the complexity of transform and quantization more efficiently than competing techniques. In addition, it is shown that the overall video quality achieved by the proposed algorithm is comparable to the references.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5934378]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>249</startPage>
			<endPage>259</endPage>
			<fileSize>7531</fileSize>
			<authors><![CDATA[Li, J.;Gabbouj, M.;Takala, J.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Color-Decoupled Photo Response Non-Uniformity for Digital Image Forensics]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5934587]]></link>
			<description><![CDATA[The last few years have seen the use of photo response non-uniformity noise (PRNU), a unique fingerprint of imaging sensors, in various digital forensic applications such as source device identification, content integrity verification, and authentication. However, the use of a color filter array for capturing only one of the three color components per pixel introduces color interpolation noise, while the existing methods for extracting PRNU provide no effective means for addressing this issue. Because the artificial colors obtained through the color interpolation process are not directly acquired from the scene by physical hardware, we expect that the PRNU extracted from the physical components, which are free from interpolation noise, should be more reliable than that from the artificial channels, which carry interpolation noise. Based on this assumption we propose a couple-decoupled PRNU (CD-PRNU) extraction method, which first decomposes each color channel into four sub-images and then extracts the PRNU noise from each sub-image. The PRNU noise patterns of the sub-images are then assembled to get the CD-PRNU. This new method can prevent the interpolation noise from propagating into the physical components, thus improving the accuracy of device identification and image content integrity verification.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5934587]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>260</startPage>
			<endPage>271</endPage>
			<fileSize>8830</fileSize>
			<authors><![CDATA[Li, C.-T.;Li, Y.;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Highly Efficient VLSI Architecture for H.264/AVC Level 5.1 CABAC Decoder]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5934379]]></link>
			<description><![CDATA[In this paper, a high throughput context-based adaptive binary arithmetic coding decoder design is proposed. This decoder employs a syntax element prediction method to solve pipeline hazard problems. It also uses a new hybrid memory two-symbol parallel decoding in order to enhance performance as well as to reduce costs. The critical path delay of the two-symbol binary arithmetic decoding engine is improved by 28% with an efficient mathematical transform. Experimental results show that the throughput of our proposed design can reach 485.76 Mbins/s in the high bit-rate coding and 446.2 Mbins/s on average at 264MHz operating frequency, which is sufficient to support H.264/AVC level 5.1 real-time decoding.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5934379]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>272</startPage>
			<endPage>281</endPage>
			<fileSize>4136</fileSize>
			<authors><![CDATA[Liao, Y.-H.;Li, G.-L.;Chang, T.-S.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Massive Parallel-Hardware Architecture for Multiscale Stereo, Optical Flow and Image-Structure Computation]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5955104]]></link>
			<description><![CDATA[Low-level vision tasks pose an outstanding challenge in terms of computational effort: pixel-wise operations require high-performance architectures to achieve real-time processing. Nowadays, diverse technologies permit a high level of parallelism and in this way researchers can address more and more complex on-chip low-level vision-feature extraction. In the state of the art, different architectures have been described that process single vision modes in real time but multiple computer vision modes are seldom conjointly computed on a single device to produce a general-purpose on-chip low-level vision system: this may be the basis for mid-level or high-level vision tasks. We present here a novel architecture for multiple-vision feature extraction that includes multiscale optical flow, disparity, energy, orientation, and phase. A high degree of robustness in real-life situations is obtained thanks to adopting phase-based models (at the cost of relatively high computing resource requirements). The high flexibility of the reconfigurable devices used allows for the exploration of different hardware configurations to deal with final target and user requirements. Making use of this novel architecture and hardware-sharing techniques we describe a co-processing board implementation as a case study. It reaches an outstanding computing power of 92.3 GigaOPS at very low power consumption (approximately 12.9 GigaOPS/W).]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=5955104]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>282</startPage>
			<endPage>294</endPage>
			<fileSize>9556</fileSize>
			<authors><![CDATA[Tomasi, M.;Vanegas, M.;Barranco, F.;Daz, J.;Ros, E.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Content-Based 3-D Mosaics for Representing Videos of Dynamic Urban Scenes]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6097048]]></link>
			<description><![CDATA[We propose a content-based 3-D mosaic (CB3M) representation for long video sequences of 3-D and dynamic urban scenes captured by a camera on a mobile platform. In the first phase, a set of parallel-perspective (pushbroom) mosaics with varying viewing directions is generated to capture both the 3-D and dynamic aspects of the scene under the camera coverage. In the second phase, a segmentation-based stereo matching algorithm is applied to extract parametric representations of the color, structure and motion of the dynamic and/or 3-D objects in urban scenes, where a lot of planar surfaces exist. Multiple pairs of stereo mosaics are used for facilitating reliable stereo matching, occlusion handling, accurate 3-D reconstruction, and robust moving target detection. CB3M is a highly compressed visual representation for a dynamic 3-D scene, and has object contents of both 3-D and motion information. Experimental results are given for various real video sequences of large-scale 3-D scenes.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6097048]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>295</startPage>
			<endPage>308</endPage>
			<fileSize>8141</fileSize>
			<authors><![CDATA[Tang, H.;Zhu, Z.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Multiple Description Video Coding Based on Hierarchical B Pictures Using Unequal Redundancy]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6104125]]></link>
			<description><![CDATA[Multiple description video coding (MDC) is one of the approaches for reducing the detrimental effects caused by transmission over error-prone networks. In this paper, a MDC model based on hierarchical B pictures is proposed to optimize the tradeoff between coding efficiency and error resilience. The model produces two descriptors by applying different MDC techniques such as duplication, spatial splitting and temporal splitting on the different frames of video sequences, taking into account unequal importance of frames at different hierarchical levels. Duplication (high redundancy) is for key frames: spatial splitting (medium redundancy) for reference B frames, and temporal splitting (low redundancy) for nonreference B frames. For one descriptor loss, the model applies different estimation methods, but for the two descriptor loss case, the same temporal estimation is employed. As a consequence, better error resilience can be achieved at high coding efficiency. The advantages of the proposed model are demonstrated in error-free and packet loss networks.]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6104125]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>309</startPage>
			<endPage>320</endPage>
			<fileSize>19247</fileSize>
			<authors><![CDATA[Tsai, W.-J.;You, H.-Y.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Special issue on emerging research and standards in next generation video coding (HEVC)]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145227]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145227]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>321</startPage>
			<endPage>321</endPage>
			<fileSize>117</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[IEEE Foundation]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145229]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145229]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>322</startPage>
			<endPage>322</endPage>
			<fileSize>320</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Scitopia.org]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145228]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145228]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>323</startPage>
			<endPage>323</endPage>
			<fileSize>270</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Quality without compromise]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145230]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145230]]></guid>
			<volume>22</volume>
			<issue>2</issue>
			<startPage>324</startPage>
			<endPage>324</endPage>
			<fileSize>324</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[IEEE Circuits and Systems Society Information]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=6145208&arnumber=6145209]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
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			<title><![CDATA[IEEE Transactions on Circuits and Systems for Video Technology information for authors]]></title>
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			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[Feb.  2012]]></pubDate>
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			<volume>22</volume>
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