Yasuaki Kanatsugu

Analysis of space-dependent characteristics of motion-compensated frame differences based on a statistical motion distribution model

Although it has been observed that motion-compensated frame differences increase toward block boundaries and overlapped block motion compensation (OBMC) has been shown to provide reduced blocking artifacts as well as improved prediction accuracy, there is almost no satisfactory theoretical basis that clearly interprets the space-dependent characteristics of motion-compensated frame differences, nor have the theoretical aspects of OBMC been investigated thoroughly. We first interpret the space-dependent characteristics of motion-compensated frame differences based on a novel statistical motion distribution model. We then apply the statistical motion distribution model to the analysis of prediction efficiency of OBMC. Through the analysis, we prove theoretically that OBMC can reduce and equalize the motion-compensated frame differences across a block. The analytical results are justified by empirical experiments with typical image sequences.

MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation

This paper proposes a stochastic approach to estimate the disparity field combined with line field. In the maximum a posteriori (MAP) method based on Markov random field (MRF) model, it is important to optimize and converge the Gibbs potential function corresponding to the perturbed disparity field. The proposed optimization method, stochastic diffusion, takes advantage of the probabilistic distribution of the neighborhood fields to diffuse the Gibbs potential space iteratively. By using the neighborhood distribution in the non-random and non-deterministic diffusion, both the estimation accuracy and the convergence speed are improved. In the paper, the hierarchical stochastic diffusion is also applied to the disparity field. The hierarchical approach reduces the memory and computational load, and increases the convergence speed of the potential space. The paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure. According to the experiments, the stochastic diffusion shows good estimation performance. The line field improves the estimation at the object boundary, and coincides with the object boundary with the useful contours. The stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and MRF models.

A high-precision camera operation parameter measurement system and its application to image motion inferring

Information about camera operations such as zoom, focus, pan, tilt and dollying is significant not only for efficient video coding, but also for content-based video representation. In this paper we describe a high-precision camera operation parameter measurement system and apply it to image motion inferring. First, we outline the implemented system which is designed to provide camera operation parameters with a high precision required for image coding applications. Second, we calibrate the camera lens to determine its exact optical properties, A pin-hole camera model with the 2nd order radial lens distortion and a two-image calibration technique are employed. Finally, we use the pan, tilt and zoom parameters measured by the system to infer image motion. The experimental results show that the inferred motion coincides with the actual motion very closely. Compared to the motion analysis techniques that estimate camera motion from video sequences, our approach does not suffer from ambiguity, thus can provide reliable and accurate image global motion. The obtained motion can be applied to image mosaicing, moving object segmentation, object-based image coding, etc.

Hierarchical stochastic diffusion for disparity estimation

This paper proposes a stochastic approach to estimate the disparity field combined with line field. In the maximum a posteriori (MAP) method based on Markov random field (MRF) model, it is important to optimize and converge the Gibbs potential function corresponding to the perturbed disparity field. The proposed optimization method, stochastic diffusion, takes advantage of the probabilistic distribution of the neighborhood fields, and diffuses the Gibbs potential space to be stable iteratively. By using the neighborhood distribution in the non-random and non-deterministic diffusion, the stochastic diffusion improves both the estimation accuracy and the convergence speed. In the paper, the hierarchical stochastic diffusion is also applied to the disparity field. The hierarchical approach reduces the memory and computational load, and increases the convergence of the potential space. The line field is the discontinuity model of the disparity field. The paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure. According to the experiments, the stochastic diffusion shows good estimation performance. The line field improves the estimation at the object boundary, and the estimated line field coincides with the object boundary with the useful contours. Furthermore, the stochastic diffusion with line field embeds the occlusion detection and compensation. And, the stochastic diffusion converges the estimated fields very fast in the hierarchical scheme. The stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and MRF models.

Robust depth-map estimation from image sequences with precise camera operation parameters

The depth-map of a scene conveys the fundamental information that is extremely useful for object segmentation, compact representation of videos, etc. We present an algorithm for robust depth-map estimation from image sequences with precise camera operation parameters. This algorithm exploits precise camera operation parameters to find correspondence points among multiple frames of an image sequence of a static scene. Our algorithm is able to deal with arbitrary camera movement. The experimental results show significant improvement in the accuracy of depth-maps compared with the conventional two-frame matching method.

A hierarchical method of MAP-based stochastic diffusion and disparity estimation

This paper talks about a hierarchical approach on stochastic diffusion in the MAP-based estimation. Stochastic diffusion has been proposed for an optimization method to minimize the potential function in the MAP-based estimation, and showed good performances in the simultaneous estimations of correspondence, line, and segmentation fields. This paper applies stochastic diffusion to the MAP-based estimation of disparity and line fields in the hierarchical scheme. The proposed hierarchical method combines two successive approximations of the disparity field and potential space at the same time. This hierarchical method propagates not only the geometric relation but also interactions of neighborhood fields. The experimental results show that the proposed hierarchical stochastic diffusion decreases the memory and computational burden and improves the estimation performances in the occluded or textureless regions.

Analysis of space-dependent characteristics of motion-compensated frame differences

To design an optimal coding algorithm, a signal-source model that accurately reflects the signal characteristics is required. Although many investigations have been made on various aspects of motion-compensated frame differences, most of them assume that motion-compensated frame differences are homogeneous. Recently, however, it was observed that the variances of motion-compensated frame differences at block boundaries tend to be larger than those at block centers. In this paper we present a theoretical interpretation of the space-dependent characteristics of motion-compensated frame differences. The analysis is based on a statistical model for motion. The analytical results are justified by empirical experiments with typical image sequences.

Development of MUSE family systems

NHK has developed three ADTV (advanced definition television) systems that comply with US Federal Communication Commissions decision to maintain compatibility with existing NTSC systems: narrow-MUSE (multiple subNyquist sample encoding), NTSC (National Television System Committee)-compatible MUSE-6, and NTSC-compatible MUSE-9. All three systems use program materials produced in the 1125/60/2:1 format converted to the 750 line format and therefore can transmit a vertical resolution of approximately 750 TV lines. Among the three systems, narrow-MUSE has the highest quality, because there are no NTSC compatibility constraints. NTSC-compatible MUSE-6 has both NTSC and 6-MHz channel compatibility. So does NTSC-compatible MUSE-9, which also provides higher moving resolution by introducing a 3-MHz augmentation channel. The authors describe the features of these systems and the band compression techniques used. >

Moving object extraction using background difference and region growing with a spatiotemporal watershed algorithm

In this paper, we describe a new method for moving object extraction based on a background image subtraction method to be applied to an object-based picture coding system. Since this method requires a background image, we have to represent the background from a moving picture that has been shot already. We propose a new method in which the background image is reproduced by a temporal histogram of each pixel in the picture and a watershed algorithm is employed for region growing to acquire precise edge detection of the moving objects. Furthermore, a spatiotemporal watershed algorithm is introduced in order to prevent judder of the extracted objects.

Analysis of overlapped block motion compensation based on a statistical motion distribution model

Overlapped block motion compensation (OBMC) has been shown to provide reduced prediction errors as well as reduced blocking artifacts compared with the conventional non-overlapped block motion compensation (NOBMC). However, there is no satisfactory theoretical basis that clearly interprets why OBMC can reduce prediction errors. We present a theoretical analysis of OBMC based on a novel statistical motion distribution model. Our analysis proves theoretically that prediction errors increase towards block boundaries and that OBMC has an error reduction and equalization property, with the errors being more reduced at block boundaries than at block centers. The analytical results are justified by empirical experiments with typical image sequences.