Xuguang Lan

Low-power and high-speed VLSI architecture for lifting-based forward and inverse wavelet transform

A low-power, high-speed architecture which performs two-dimension forward and inverse discrete wavelet transform (DWT) for the set of filters in JPEG2000 is proposed by using a line-based and lifting scheme. It consists of one row processor and one column processor each of which contains four sub-filters. And the row processor which is time-multiplexed performs in parallel with the column processor. Optimized shift-add operations are substituted for multiplications, and edge extension is implemented by an embedded circuit. The whole architecture which is optimized in the pipeline design way to speed up and achieve higher hardware utilization has been demonstrated in FPGA. Two pixels per clock cycle can be encoded at 100 MHz. The architecture can he used as a compact and independent IP core for JPEG2000 VLSI implementation and various real-time image/video applications.

A peer-to-peer architecture for efficient live scalable media streaming on internet

This paper presents a manageable overlay network architecture SVCP2P for live scalable media streaming. Every peer in SVCP2P periodically exchanges data availability information with one of distributed central servers which act as the centralized index for storing peer list, program list and buffer information of peers. An efficient scheduling algorithm is proposed, which achieves real-time and continuous transmission of the scalable streaming. There are three characteristics of this architecture: 1) easy management; 2) efficient to heterogeneous network because of the scalable media streaming adapting to the heterogeneous demand; and 3) robust and resilient. We have examined the SVCP2P which has been implemented based on the IP Internet over LAN, and the results demonstrate the efficiency of SVCP2P.

Key object-based static video summarization

In this paper, we present a system for object-based video summarization facilitated by an efficient video object segmentation system. We eliminate the redundancy not only from spatial and temporal domain, but also from content domain. First, we detect shot boundaries and extract video objects by a 3D graph-based algorithm. Once the objects are obtained, the shape of the objects need to be represented. The key objects are extracted in a global manner by K-means clustering of shapes. Experimental results on the proposed object-based scheme combined with efficient video object segmentation show desirable summarization.

Arbitrary ROI-Based Wavelet Video Coding

An arbitrary shape region of interest (ROI) coding is presented for scalable wavelet video codec in this paper. The padding of macroblock and polygon matching are employed to estimate the motion of the ROI. The motion vectors derived are set as the motion trajectory of the samples to generate one-dimensional temporal signal, filtered to reduce the temporal redundancy by using motion compensated temporal filtering for arbitrary shape ROI. The Reconstructed quality of the ROI coding can be significantly improved at low bit rate, compared to non-ROI coding. The efficiency of the MCTF based on arbitrary ROI is compared with that of the video object coding in MPEG-4. The ability of the MCTF to reduce the temporal redundancy is better than or comparable to that MPEG-4 to some extent.

Spatio-temporal saliency perception via hypercomplex frequency spectral contrast.

Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC). Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value) color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms.

A Peer-to-Peer Architecture for Live Streaming with DRM

DRM is becoming more and more important for P2P live streaming. In this paper, a manageable overlay network architecture with DRM, is proposed for live streaming. The system consists of register server, index servers, supernodes and peers. The register server authorizes the peers, assign the key and index server list; The index server acts as the centralized index server to store the peer list, program list, and buffer information of peers. Supernodes are special peers which store the bigger buffer of live streaming. Each peer periodically exchanges data availability information with the assigned index server. The peer can retrieve correspondingly unavailable data from partners given by index server using the proposed scheduling algorithm. It also supports DRM in register server. The proposed system has been demonstrated based on CERNET of China. Good streaming quality can be achieved due to its global optimization and the digital right of video contents can be protected to some extent.

Efficient Lossy Compression for Compressive Sensing Acquisition of Images in Compressive Sensing Imaging Systems

Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image acquisition, the general image compression solution may not work well. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI. First, we design an adaptive compressive sensing acquisition method for images according to the sampling rate, which could achieve better CS reconstruction quality for the acquired image. Second, we develop a universal quantization for the obtained CS measurements from CS acquisition without knowing any a priori information about the captured image. Finally, we apply these two methods in the CSI system for efficient lossy compression of CS acquisition. Simulation results demonstrate that the proposed solution improves the rate-distortion performance by 0.4∼2 dB comparing with current state-of-the-art, while maintaining a low computational complexity.

Depth-Assisted Temporal Error Concealment for Intra Frame Slices in 3-D Video

We propose a depth-assisted error concealment method for slice losses in intra frames of 2-D+depth encoded 3-D video. Intra frames in the depth sequence are offset from intra frames in the 2-D view sequence, such that the corresponding frames in the other sequence are not also intra mode. For slice losses in an intra frame of the 2-D sequence, motion vector (MV) candidates to conceal the lost macroblocks come from the co-located MVs in the depth frame as well as from MVs in the previous 2-D frame chosen based on motion information from the depth frame. Then the temporal smoothness of the depth sequence is used to filter out MV candidates. Finally, an enhanced distortion criterion is used for MV selection, based on contour information in the depth frame and boundary matching. Experimental results show that the proposed method provides improved performance over existing methods.

Manageable peer-to-peer architecture for video-on-demand

An efficiently manageable overlay network architecture, called AIRVoD, is proposed for video-on- demand, based on a distributed-centralized P2P network. The system consists of distributed servers, peers, SuperNodes and sub-SuperNodes. The distributed central servers act as the centralized index server to store the peer list, program list, and buffer information of peers. Each newly joined peer in AIRVoD periodically exchanges data availability information with the central server. Some powerful peers are selected to be sub-SuperNodes which store a larger part of the demanded program. The demanding peers can retrieve correspondingly unavailable data from partners selected from the central server, sub- SuperNodes and SuperNodes that have the original programs to supply the available data, by using the proposed parallel scheduling algorithm. There are four characteristics of this architecture: 1) easy to globally balance and manage: central server can identify, and cluster each newly joined peer, and allocate the load in the whole peer network; 2) efficient for dynamic networks: data transmission is dynamically determined according to data availability which can be derived from central server; 3) resilient, as the partnerships can adapt to quick switching among multi-suppliers under global balance; and 4) highly cost-effective: the powerful peers are taken full advantage to be larger suppliers. AIRVoD has been demonstrated based on CERNET OF CHINA.

3D spatio-temporal graph cuts for video objects segmentation

In this paper, we present a method to extract moving objects in monocular image sequences. The proposed method is based on graph cuts defined on a spatio-temporal region adjacency graph (RAG). First, we initially over-segment each frame in the video, and take the over-segmented regions as the vertices in the 3D spatio-temporal graph. Second, multiple cues are fused together to extract objects accurately. Finally, accurate foreground/background segmentation are efficiently achieved by binary graph cut. The experimental results showed that the proposed method improved the performance of segmentation with respect to the popular methods.