Catálogo de publicaciones - libros

This paper presents a predictive-coding algorithm for the compression of multiple depth-sequences obtained from a multi-camera acquisition setup. The proposed depth-prediction algorithm works by synthesizing a virtual depth-image that matches the depth-image (of the predicted camera). To generate this virtual depth-image, we use an image-rendering algorithm known as 3D image-warping. This newly proposed prediction technique is employed in a 3D coding system in order to compress multiview depth-sequences. For this purpose, we introduce an extended H.264 encoder that employs two prediction techniques: a block-based motion prediction and the previously mentioned 3D image-warping prediction. This extended H.264 encoder adaptively selects the most efficient prediction scheme for each image-block using a rate-distortion criterion. We present experimental results for several multiview depth-sequences, which show a quality improvement of about 2.5 dB as compared to H.264 inter-coded depth-images.

Current image enhancement in televisions can be improved if the image is analyzed, objects of interest are segmented, and each segment is processed with specifically optimized algorithms. In this paper we present an algorithm and feature model for segmenting grass areas in video sequences. The system employs adaptive color and position models for creating a coherent grass segmentation map. Compared with previously reported algorithms, our system shows significant improvements in spatial and temporal consistency of the results. This property makes the proposed system suitable for TV video applications.

The heterogeneity in the contemporary multimedia environments requires a format-agnostic adaptation framework for the consumption of digital video content. Preferably, scalable bitstreams are used in order to satisfy as many circumstances as possible. In this paper, the scalable extension on the H.264/AVC specification is used to obtain the parent bitstreams. The adaptation along the combined scalability axis of the bitstreams must occur in a format-independent manner. Therefore, an abstraction layer of the bitstream is needed. In this paper, XML descriptions are used representing the high-level structure of the bitstreams by relying on the MPEG-21 Bitstream Syntax Description Language standard. The adaptation process is executed in the XML domain by transforming the XML descriptions considering the usage environment. Such an adaptation engine is discussed in this paper in which all communication is based on XML descriptions without knowledge of underlying coding format. From the performance measurements, one can conclude that the transformations in the XML domain and the generation of the corresponding adapted bitstream can be realized in real time.

MPEG-4, which is a video coding standard, supports object-based functionalities for high efficiency coding. MPEG-7, a multimedia content description interface, handles the object data in, for example, retrieval and/or editing systems. Therefore, extraction of semantic video objects is an indispensable tool that benefits these newly developed schemes. In the present paper, we propose a technique that extracts the shape of moving objects by combining snakes and watershed algorithm. The proposed method comprises two steps. In the first step, snakes extract contours of moving objects as a result of the minimization of an energy function. In the second step, the conditional watershed algorithm extracts contours from a topographical surface including a new function term. This function term is introduced to improve the estimated contours considering boundaries of moving objects obtained by snakes. The efficiency of the proposed approach in moving object extraction is demonstrated through computer simulations.

With the growing demand for low delay video streaming in error-prone environments, error resilience tools, such as the data partitioning tool in the H.264/AVC specification, are becoming more and more important. In this paper, the introduction of constrained inter prediction into the H.264/AVC specification is proposed. Constrained inter prediction can help the data partitioning tool by removing the dependencies between partitions B and C, thereby making it possible to process partition C if partition B is lost. From the experimental results it is observed that the cost for introducing this technique can be neglected. Furthermore, when constrained inter prediction is used in combination with constrained intra prediction, resulting bitstreams have an increased peak signal-to-noise ratio of up to 1.8 dB in error-prone environments compared to when only constrained intra prediction is used.

Currently H.264/AVC supports variable block motion compensation, multiple reference images, 1/4-pixel motion vector accuracy, and in-loop deblocking filter, compared with the existing compression technologies. While these coding technologies are major functions of compression rate improvement, they lead to high complexity at the same time. For the H.264 video coding technology to be actually applied on low-end / low-bit rates terminals more extensively, it is essential to improve the coding speed. Currently the deblocking filter that can improve the moving picture’s subjective image quality to a certain degree is used on low-end terminals to a limited extent due to computational complexity. In this paper, a performance improvement method of the deblocking filter that efficiently reduces the blocking artifacts occurred during the compression of low-bit rates digital motion pictures is suggested. Blocking artifacts are plaid images appear on the block boundaries due to DCT and quantization. In the method proposed in this paper, the image’s spatial correlational characteristics are extracted by using the variable block information of motion compensation; the filtering is divided into 4 modes according to the characteristics, and adaptive filtering is executed in the divided regions. The proposed deblocking method reduces the blocking artifacts, prevents excessive blurring effects, and improves the performance about 40% compared with the existing method.

Road signs recognition systems are developed to assist drivers and to help increase traffic safety. Shape detectors constitute a front-end in majority of such systems. In this paper we propose a method for robust detection of triangular, rectangular and rhombus shaped road signs in real traffic scenes. It starts with segmentation of colour images. For this purpose the histograms were created from hundreds of real warning and information signs. Then the characteristic points are detected by means of the developed symmetrical detector of local binary features. The points are further clusterized and used to select shapes from the input images. Finally, the shapes are verified to fulfil geometrical properties defined for the road signs. The proposed detector shows high accuracy and very fast operation time what was verified experimentally.

In this paper, we consider high-resolution multi-sprite generation and its application to background sprite coding. Firstly, we propose an approach to partitioning a video sequence into multiple background sprites and selecting an optimal reference frame for each sprite range. This approach groups images that cover a similar scene into the same sprite range. We then propose an iterative regularized technique for constructing a high-resolution sprite in each sprite range. This technique determines the regularization parameter automatically and produces sprite images with high visual quality. Due to the advantages of high-resolution multi-sprites, a high-resolution sprite coding method is also presented and it achieves high coding efficiency.

This paper proposes an adaptive motion mode selection method in H.264 frame skipping transcoding. In order to reduce the high complexity arising from variable block sizes in H.264, the proposed method exploits original motion information from incoming bitstreams. In addition, the paper also adopts Forward Dominant Vector Selection approach in MV composition of H.264 transcoding, in comparison with Bilinear Interpolation method. The simulation results show that the proposed method achieves good trade-off between computational complexity and video quality.

The first step in various computer vision applications is a detection of moving objects. The prevalent pixel-wise models regard image pixels as independent random processes. They don’t take into account the existing correlation between the neighboring pixels. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, this correlation can be exploited to achieve high levels of detection accuracy in the presence of dynamic backgrounds. This work improves recently proposed joint domain-range model for the background subtraction, which assumes the constant kernel bandwidth. The improvement is obtained by adapting the kernel bandwidth according to the local image structure. This approach provides the suppression of structural artifacts present in detection results when the kernel density estimation with constant bandwidth is used. Consequently, a more accurate detection of moving objects can be achieved.