The correct identification of the intima-media thickness (IMT) of the common carotid artery (CCA) walls has a high clinical relevance as it represents one of the most reliable predictor for future cardiovascular events. In this work we propose and evaluate an integrated system for the segmentation of the intima-media complex (IMC) and the lumen diameter in longitudinal ultrasound video of the CCA based on normalization, speckle reduction filtering (with a first order statistics filter) and snakes segmentation. The algorithm is initialized in the first video frame of the cardiac cycle by an automated initialization procedure and the borders of the far wall and near wall of the CCA are estimated. The IMC and the carotid diameter are then segmented automatically in the consecutive video frames for one cardiac cycle. The proposed algorithm was evaluated on 10 longitudinal ultrasound B-mode videos of the CCA and is compared with the manual tracings of a neurovascular expert, for every 20 frames in a time span of 3-5 seconds, covering in general 1-2 cardiac cycles. The algorithm estimated an IMT mean ± standard deviation of (0.72±0.22) mm while the manual results were (0.70±0.19). The mean maximum and minimum diameter was (7.08±1.37) mm and (6.53±1.13) mm respectively. The results were validated based on statistical measures and univariate statistical analysis. It was shown that there was no significant difference between the snakes segmentation measurements and the manual measurements. The proposed integrated system could successfully segment the IMC in ultrasound CCA video sequences thus complementing manual measurements.

No abstract

Recent advances in video compression and 3D displays have necessitated a further understanding and development of 3D video coding algorithms. The emergence of low cost autostereoscopic displays is expected to drive the growth of 3DTV services. This paper discusses key issues that affect the quality of 3D video experience on autostereoscopic displays. The characteristics of the human visual system can be exploited to compress individual stereo views at different qualities without affecting the perceptual quality of the 3D video. The H.264/AVC video coding algorithm was used to compress each view. We examine the bounds of asymmetric stereo view compression and its relationship to eyedominance based on a user study. This paper also presents the design and development of a modular video player with stereoscopic and multi-view capabilities including a discussion of useful tools for accelerating the development and enhancing flexibility. The experimental results indicate that eye-dominance influences 3D perception and as a result will impact the coding efficiency of 3D video.

No abstract

H.264 is a highly efficient and complex video codec. The complexity of the codec makes it difficult to use all its features in resource constrained mobile devices. This paper presents a machine learning approach to reducing the complexity of Intra encoding in H.264. Determining the macro block coding mode requires substantial computational resources in H.264 video encoding. The goal of this work to reduce MB mode computation from a search operation, as is done in the encoders today, to a computation. We have developed a methodology based on machine learning that computes the MB coding mode instead of searching for the best match thus reducing the complexity of Intra 16x16 coding by 17 times and Intra 4x4 MB coding by 12.5 times. The proposed approach uses simple mean value metrics at the block level to characterize the coding complexity of a macro block. A generic J4.8 classifier is used to build the decision trees to quickly determine the mode. We present a methodology for Intra MB coding. The results show that intra MB mode can be determined with over 90% accuracy. The proposed can also be used for determining MB prediction modes with an accuracy varying between 70% and 80%. INTRODUCTIONRecent developments in video encoding such as the H.264 and VC1 have resulted in highly efficient compression. These new generation codecs are highly efficient and result in equivalent quality video at 1/3 to ½ of MPEG-2 video bitrates. The complexity of this new encoder, however, is 10 times as complex [1]. The compression efficiency has a high computational cost associated with it. The high computational cost is the key reason why these increased compression efficiencies cannot be exploited across all application domains. Resource constrained devices such as cell phones, embedded cameras, and video sensors use simpler encoders or simpler profiles of new codecs to tradeoff compression efficiency and quality for reduced complexity. The new video codecs from Microsoft and Real are also based on hybrid coding techniques similar to H.264 and are comparable in complexity and quality.The compressions efficiency of these new codecs has increased mainly because of the large number of coding options available. For example, the H.264 video supports Intra prediction with 3 different block sizes and Inter prediction with 8 different block sizes. The encoding of a macro block (MB) involves evaluating all the possible block sizes. As the number of reference frames is increased, the complexity increases proportionally. New approaches are necessary to drastically reduce the encoding complexity without sacrificing quality. We are developing low complexity encoding tools based on machine learning techniques. The goal is to reduce MB mode and motion vector search to a decision tree with a motion search in a small window. The proposed approach is based on the hypothesis that video frames can be characterized for the purpose of encoding and the encoding complexity can be drastically reduced. Our work on MPEG-2 to H.264 transcoding has shown that ma...