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This paper presents a statistical analysis of several key factors in H.264/AVC motion estimation, including the relationship between sum of absolute difference and sum of absolute transformed difference (SATD) in macroblock (MB) level, the changes of SATD from full-pixel precision to quarter-pixel precision, and so on. Based on these statistical characteristics, we proved the validity of a fractional motion estimation (FME) mode reduction criterion. During motion estimation, any MBs satisfying this criterion can keep their integer motion estimation best mode until after FME, thus avoiding many costly less probable prediction modes in the FME stage. Simulation results confirm that prediction schemes using this criterion have a very low miss rate in mode determination, FME speed-up is achieved and the peak signal-to-noise ratio drop is almost negligible. [ABSTRACT FROM PUBLISHER]

Video transmitted over an error-prone network may be received at the decoder with degradations due to packet losses. No-reference quality monitoring algorithms are the most practical way to measure the quality of the received video, since they do not impose any change with respect to the network architecture. Conventionally, these methods assume the availability of the corrupted bitstream. In some situations this is not possible, e.g., because the bitstream is encrypted or processed by third-party decoders, and only the decoded pixel values can be used. The major issue in this scenario is the lack of knowledge about which regions of the video have been actually lost, which is a fundamental ingredient for estimating channel-induced distortion. In this paper, we propose a maximum a posteriori estimation of the pattern of lost macroblocks, which assumes the knowledge of the decoded pixels only. This information can be used as input to a no-reference quality monitoring system, which produces an accurate estimate of the mean-square-error (MSE) distortion introduced by channel errors. The results of the proposed method are well correlated with the MSE distortion computed in full-reference mode, with a linear correlation coefficient equal to 0.9 at frame level and 0.98 at sequence level. [ABSTRACT FROM PUBLISHER]

In this paper, we develop a high-frame-rate (HFR) vision system that can estimate the optical flow in real time at 1000 f/s for 1024\,\times\,1024 pixel images via the hardware implementation of an improved optical flow detection algorithm on a high-speed vision platform. Based on the Lucas–Kanade method, we adopt an improved gradient-based algorithm that can adaptively select a pseudo-variable frame rate according to the amplitude of the estimated optical flow to accurately detect the optical flow for objects moving at high and low speeds in the same image. The performance of our developed HFR optical flow system was verified through experimental results for high-speed movements such as a top's spinnning motion and a human's pitching motion. [ABSTRACT FROM PUBLISHER]

Motion detection is the first essential process in the extraction of information regarding moving objects and makes use of stabilization in functional areas, such as tracking, classification, recognition, and so on. In this paper, we propose a novel and accurate approach to motion detection for the automatic video surveillance system. Our method achieves complete detection of moving objects by involving three significant proposed modules: a background modeling (BM) module, an alarm trigger (AT) module, and an object extraction (OE) module. For our proposed BM module, a unique two-phase background matching procedure is performed using rapid matching followed by accurate matching in order to produce optimum background pixels for the background model. Next, our proposed AT module eliminates the unnecessary examination of the entire background region, allowing the subsequent OE module to only process blocks containing moving objects. Finally, the OE module forms the binary object detection mask in order to achieve highly complete detection of moving objects. The detection results produced by our proposed (PRO) method were both qualitatively and quantitatively analyzed through visual inspection and for accuracy, along with comparisons to the results produced by other state-of-the-art methods. The analyses show that our PRO method has a substantially higher degree of efficacy, outperforming other methods by an F1 metric accuracy rate of up to 53.43%. [ABSTRACT FROM PUBLISHER]

Wyner–Ziv video coding (WZVC) is a newly emerged video coding scheme which compresses the input video frames with the side information (SI) frames only available at the decoder. WZVC exploits the statistics between the source frame and the SI frame at the decoder by utilizing their correlation information. This correlation information is important but also difficult to estimate due to the absence of the SI frame at the encoder, and the lack of the source frame at the decoder. In this paper, we focus on this problem and propose a novel transform-domain adaptive correlation estimation method called TRACE for WZVC. In TRACE, the correlation information is progressively learned during the decoding process of each frame. Within TRACE, we also propose a convex optimization based band-level correlation estimation method which is optimal in the sense of minimizing the theoretical bit rate. Experiments suggest that, when applied in motion compensated interpolation-based low complexity WZVC, TRACE yields competitive results against the state-of-the-art correlation estimation algorithms. More importantly, different from the existing coefficient-level correlation estimation algorithms, the proposed TRACE can be applied in many other WZVC schemes and can provide considerable gain over the popular band-level correlation estimation methods. [ABSTRACT FROM PUBLISHER]

In this paper, we present a framework for real-time mosaicing from video sequences recorded from an uncalibrated pan tilt zoom camera based on multiframe registration. To this end, a new frame alignment algorithm, the direct local indirect global (DLIG), is presented. The key idea of the DLIG alignment is to divide the frame alignment problem into the problem of registering a set of spatially related image patches. The registration is iteratively computed by sequentially imposing a good local match and global spatial coherence. The patch registration is performed using a tracking algorithm, so a very efficient local matching can be achieved. We use the patch-based registration to obtain multiframe registration, using the mosaic coordinates to relate the current frame to patches from different frames that partially share the current field of view. Multiframe registration prevents the error accumulation problem, one of the most important problems in mosaicing. We also show how to embed a kernel tracking algorithm in order to obtain a precise and extremely efficient mosaicing algorithm. Finally, we perform a quantitative evaluation of our algorithm, including a comparison with other alignment approaches, and studying its performance against interlaced videos and illumination changes. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm for moving object detection and tracking. The proposed algorithm includes two schemes: one for spatio-temporal spatial segmentation and the other for temporal segmentation. A combination of these schemes is used to identify moving objects and to track them. A compound Markov random field (MRF) model is used as the prior image attribute model, which takes care of the spatial distribution of color, temporal color coherence and edge map in the temporal frames to obtain a spatio-temporal spatial segmentation. In this scheme, segmentation is considered as a pixel labeling problem and is solved using the maximum a posteriori probability (MAP) estimation technique. The MRF-MAP framework is computation intensive due to random initialization. To reduce this burden, we propose a change information based heuristic initialization technique. The scheme requires an initially segmented frame. For initial frame segmentation, compound MRF model is used to model attributes and MAP estimate is obtained by a hybrid algorithm [combination of both simulated annealing (SA) and iterative conditional mode (ICM)] that converges fast. For temporal segmentation, instead of using a gray level difference based change detection mask (CDM), we propose a CDM based on label difference of two frames. The proposed scheme resulted in less effect of silhouette. Further, a combination of both spatial and temporal segmentation process is used to detect the moving objects. Results of the proposed spatial segmentation approach are compared with those of JSEG method, and edgeless and edgebased approaches of segmentation. It is noticed that the proposed approach provides a better spatial segmentation compared to the other three methods. [ABSTRACT FROM AUTHOR]

For multiview video generation from 2-D video sequences, the most important stage is to efficiently synthesize the corresponding depth maps for all video frames. The depth map generation involving precise object segmentation and depth assignment requires intensive computation and human assistance which could be costly and possibly inconsistent. To achieve reliable depth map generation, an efficient depth map interpolation method from the existing pairs of keyframes and their corresponding depth maps at the both ends of video scene is presented. The proposed method contains forward/backward motion alignment, block-based updating, object-based updating, frame selection mechanism, and depth adjustment. Experimental results show that the proposed method can generate the depth maps successfully after objective and subjective evaluations. We believe that it could greatly help to achieve multiview video generation from 2-D video sequences effectively. [ABSTRACT FROM AUTHOR]