Real-time streaming of audiovisual content over wireless LANs (WLANs) is emerging as an important technology area in multimedia communications. Due to the error-prone, time-varying characteristics of wireless channels, there is a need for strong protection of video bitstreams. To cope with the variation of channel conditions, we introduce a novel cross-layer protection strategy that combines adaptive application-layer forward error correction (FEC) and physical-layer modulation with fine-granular-scalability (FGS) coding to improve the robustness of wireless transmission. Unlike data streams, different parts of a video stream have different priorities and hence merit the use of unequal error protection. Our schemes can dynamically select the optimal combination of FEC and modulation based on channel conditions and video data content. Experimental results show the stability of the scheme over a variety of channel conditions.

The amount of digital multimedia content available to consumers is growing because of the existence of digital capturing devices such as digital cameras, camcorders and the advent of digital video broadcast. With this increase in content, it becomes important for users to be able to browse and search for content in a timely manner. Descriptions and annotations of the content are needed to enable searching and browsing of content. MPEG-7 is a recent ISO standard for multimedia content description. In this paper, we present three descriptors, which we had proposed to MPEG-7, and have been accepted to the standard. In addition, we describe algorithms for automatically extracting these descriptors. We also present the indexing and retrieval algorithms that we developed for these descriptors; these algorithms are fast and scalable for large databases. The results presented in the paper for image and video segment matching using these descriptors show their usefulness in real applications.

Robust streaming of video over the IEEE 802.11 wireless LANs (WLANs) poses many challenges, including coping with bandwidth variations and data losses. To address these challenges, we evaluate and compare various error control strategies in this paper, namely, medium access control (MAC) layer forward error correction (FEC), MAC retransmission, and application-layer FEC, combined with FGS scalable video coding to achieve reliable communication over the IEEE 802.11 WLANs. Moreover, we assess the performance of fine-grained loss protection (FGLP) for different multipath channel conditions. Furthermore, the performance of cross-layer strategies such as application-layer FEC with MAC retransmissions is also determined.

Robust streaming of video over 802.11 wireless local area networks poses many challenges, including coping with bandwidth variations, data losses, and heterogeneity of the receivers. Currently, each network layer (including physical layer, media access control (MAC), transport, and application layers) provides a separate solution to these challenges by providing its own optimized adaptation and protection mechanisms. However, this layered strategy does not always result in an optimal overall performance for the transmission of video. Moreover, certain protection strategies can be implemented simultaneously in several layers and, hence, the optimal choices from the application and complexity perspective need to be identified. In this paper, we evaluate different error control and adaptation mechanisms available in the different layers for robust transmission of video, namely MAC retransmission strategy, application-layer forward error correction, bandwidth-adaptive compression using scalable coding, and adaptive packetization strategies. Subsequently, we propose a novel adaptive cross-layer protection strategy for enhancing the robustness and efficiency of scalable video transmission by performing tradeoffs between throughput, reliability, and delay depending on the channel conditions and application requirements. The results obtained using the proposed adaptive cross-layer protection strategies show a significantly improved visual performance for the transmitted video over a variety of channel conditions.

Joint representation of color-based features for multiple images or a video segment is an important task for visual information management systems. Generally, a key-frame or key-image is selected from such a group, and the color-related features of the entire collection are represented with those of the chosen sample. Such methods are highly dependent on the quality of the representative sample, and may lead to unreliable results. We present a set of histogram-based descriptors that reliably capture the color content of multiple images or video frames. These descriptors are defined for a group-of-frames (GoF) or a group-of-pictures (GoP). A single representation for the entire collection is obtained by combining individual frame or image histograms in various ways. We demonstrate the efficacy of GoF-histograms for video segment retrieval and the GoP-histograms for fast image search. This descriptor has been accepted to the Working Draft of MPEG-7, the evolving ISO standard for multimedia content description.

Digital images and video clips are becoming popular due to the increase in the availability of consumer devices that capture digital images and video clips. Digital content is also growing over the Internet. The increase of the digital content creates a need for user-friendly tools to browse through large volumes of digital material. We present a clustering-based browsing algorithm. Images are automatically clustered using a hierarchical clustering algorithm and users can then browse through the images by navigating the tree structure that results from the clustering. We have tested the algorithm on a large number of images.

This paper presents multiresolution models for Gauss-Markov random fields (GMRFs) with applications to texture segmentation. Coarser resolution sample fields are obtained by subsampling the sample field at fine resolution. Although the Markov property is lost under such resolution transformation, coarse resolution non-Markov random fields can be effectively approximated by Markov fields. We present two techniques to estimate the GMRF parameters at coarser resolutions from the fine resolution parameters, one by minimizing the Kullback-Leibler distance and another based on local conditional distribution invariance. We also allude to the fact that different GMRF parameters at the fine resolution can result in the same probability measure after subsampling and present the results for first- and second-order cases. We apply this multiresolution model to texture segmentation. Different texture regions in an image are modeled by GMRFs and the associated parameters are assumed to be known. Parameters at lower resolutions are estimated from the fine resolution parameters. The coarsest resolution data is first segmented and the segmentation results are propagated upward to the finer resolution. We use the iterated conditional mode (ICM) minimization at all resolutions. Our experiments with synthetic, Brodatz texture, and real satellite images show that the multiresolution technique results in a better segmentation and requires lesser computation than the single resolution algorithm.

Traditionally, Markov random field (MRF) models have been used in low-level image analysis. The article presents an MRF-based scheme to perform object delineation. The proposed edge-based approach involves extracting straight lines from the edge map of an image. Then, an MRF model is used to group these lines to delineate buildings in aerial images.

A multiresolution model for Gauss Markov random fields (GMRF) is presented. Coarser resolution sample fields are obtained by either subsampling or local averaging the sample field at the fine resolution. Although Markovianity is lost under such resolution transformation, coarser resolution non-Markov random fields can be effectively approximated by Markov fields. We use a local conditional distribution invariance approximation, to estimate the parameters of the coarser resolution processes from the fine resolution parameters. This multiresolution model is used to perform texture segmentation.

A multichannel scheme for texture segmentation using Gauss Markov random fields (GMRF) is presented. We present a family of filters called Markov filters with a property that, when a homogeneous GMRF is filtered with these filters, the resulting output is also a GMRF. We use these filters to decompose images in a fashion similar to the wavelet decomposition, such that the individual subbands are also GMRFs. However, in wavelet decomposition, after filtering, the individual subbands are subsampled and Markov fields lose the Markov property when subsampled. We have shown in [Krishnamachari and Chellappa, 1995] that subsampled GMRFs can be efficiently approximated by Markov fields using the local conditional distribution invariance approximation. Hence individual subbands can be modeled by GMRFs. We have used this multichannel model to classify remote sensed imagery and to perform texture segmentation.

An energy function based approach is presented to detect rectangular shapes in images. The proposed edge-based approach involves extracting straight lines from an edge map of the image. Then a Markov Random Field (MRF) is built on these lines. The energy function associated with the MRF can be construed as a measure of the conditional probability of observing the lines given the rectangular shapes (the positions and number of which are unknown) in the image and the minimization results in a maximum likelihood estimate. This approach, supplemented with some qualitative information about shadows and gradients, is used to detect rectangular buildings in real aerial images. Due to poor quality of the real images, only partial shapes are extracted in some cases. A modified deformable contour (snakes) based approach is then presented for completion of the partial shapes.>