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This study proposes a novel fuzzy quantization based bit transform for low bit-resolution motion estimation. We formalize the procedure of bit resolution reduction by two successive steps, namely interval partitioning and interval mapping. The former is a many-to-one mapping which determines motion estimation performance, while the latter is a one-to-one mapping. To gain a reasonable interval partitioning, we propose a non-uniform quantization method to compute coarse thresholds. They are then refined by using a membership function to solve the mismatch of pixel values near threshold caused by camera noise, coding distortion, etc. Afterwards, we discuss that the sum of absolute difference (SAD) is one of the fast matching metrics suitable for low bit-resolution motion estimation in the sense of mean squared errors. A fuzzy quantization based low bit-resolution motion estimation algorithm is consequently proposed. Our algorithm not only can be directly employed in video codecs, but also be applied to other fast or complexity scalable motion estimation algorithms. Extensive experimental results show that the proposed algorithm can always achieve good motion estimation performances for video sequences with various characteristics. Compared with one-bit transform, multi-thresholding two-bit transform, and adaptive quantization based two-bit transform, our bit transform separately gains 0.98dB, 0.42dB, and 0.24dB improvement in terms of average peak signal-to-noise ratio, with less computational cost as well.

Previous video summarization studies focused on monocular videos, and the results would not be good if they were applied to multi-view videos directly, due to problems such as the redundancy in multiple views. In this paper, we present a method for summarizing multi-view videos. We construct a spatio-temporal shot graph and formulate the summarization problem as a graph labeling task. The spatio-temporal shot graph is derived from a hypergraph, which encodes the correlations with different attributes among multi-view video shots in hyperedges. We then partition the shot graph and identify clusters of event-centered shots with similar contents via random walks. The summarization result is generated through solving a multi-objective optimization problem based on shot importance evaluated using a Gaussian entropy fusion scheme. Different summarization objectives, such as minimum summary length and maximum information coverage, can be accomplished in the framework. Moreover, multi-level summarization can be achieved easily by configuring the optimization parameters. We also propose the multi-view storyboard and event board for presenting multi-view summaries. The storyboard naturally reflects correlations among multi-view summarized shots that describe the same important event. The event-board serially assembles event-centered multi-view shots in temporal order. Single video summary which facilitates quick browsing of the summarized multi-view video can be easily generated based on the event board representation.

In this paper, we propose a visually lossless accuracy model for scalable coding of motion vectors in overcomplete wavelet-based scalable video codec. By exploiting theory of stationary random process, we first estimate motion compensation errors in spatial domain due to inaccurate motion vectors. We then extend the results to overcomplete wavelet domain, and further derive the errors caused by fraction-pixel motion vectors. Finally, combining with a visibility model of wavelet coefficient errors, we propose a novel algorithm to estimate the accuracy threshold of motion vectors with which motion compensation errors will be invisible. Experimental results show that our algorithm is effective in estimating visually lossless accuracy threshold of motion vectors. The proposed algorithm can also be used in scalable motion estimation in wavelet domain. It can accelerate motion estimation speed by stopping halfway at the accuracy that will not cause any visible errors.

In this paper, we present a novel approach to motion estimation (ME) of arbitrarily shaped video objects in wavelet domain. We explore the guiding role of binary alpha-plane in assisting ME of video objects and first devise a new block matching scheme of alpha-plane, by exploiting boundary expansion and boundary masks. To eliminate shift-variance, we modify low-band-shift (LBS) method via substituting variable-size block for wavelet block. Combining the modified LBS with a hierarchical structure, we further present a multiscale ME approach. Extensive experiments show that the proposed approach outperforms most of previous methods in terms of both subjective quality and objective quality. Moreover, significant reduction is achieved in computational complexity (89.05% at most) and memory requirement.

This paper proposes an accuracy-distortion model for overcomplete wavelet-based scalable motion estimation by exploiting the theory of stationary random process. We first estimate the motion compensation errors in spatial domain due to inaccurate motion vectors, then extend the results to overcomplete wavelet domain, and further derive the errors caused by fraction-pixel motion vectors. Finally, combining with a visibility model of wavelet coefficient errors, this paper proposes a novel algorithm to estimate the motion vector accuracy with which the motion compensation errors will be invisible. Experimental results show that the proposed algorithm is effective in estimating the visually lossless accuracy threshold of motion vectors. The proposed algorithm can be used in scalable coding of motion vectors. Also it can accelerate the motion estimation speed by stopping halfway at the accuracy that will not cause any visible errors.

In this paper, we propose a fast motion estimation algorithm of arbitrarily shaped video object in MPEG-4. The proposed algorithm incorporates the binary alpha-plane and the extended contour to predict accurately the motion vectors of boundary macroblocks so that the conventional fast motion estimation algorithms can be employed to search the motion vectors of opaque macro-blocks using the motion vectors of the neighboring boundary macro-blocks as the initial center. Experimental results show that the proposed algorithm requires low computation complexity while provides good motion compensation quality