A capacity estimation technique for JPEG-to-JPEG image watermarking
In JPEG-to-JPEG image watermarking (J2J), the input is a JPEG image file. After watermark embedding, the image is JPEG-compressed such that the output file is also a JPEG file. We use the human visual system (HVS) model to estimate the J2J data hiding capacity of JPEG images, or the maximum number of bits that can be embedded in JPEG-compressed images.  Watson's HVS model (Proc. SPIE Human Vision, Visual Process., and Digital Display IV, p.202-16, 1993) is modified to estimate the just noticeable difference (JND) for DCT coefficients. The number of modifications to DCT coefficients is limited by JND in order to guarantee the invisibility of the watermark. Our capacity estimation method does not assume any specific watermarking method and thus would apply to any watermarking method in the J2J framework.

title: On improving the iterative watermark embedding technique for JPEG-to-JPEG watermarking
abstract: We previously proposed an iterative technique called iterative watermark embedding (IWE) for watermark embedding in JPEG compressed domains. The proposed technique embeds the watermark successfully when the quality factor of the JPEG image is high, but when the quality factor is low, not all the bits of the watermark can be embedded. This is due to uneven distribution of capacities in different regions of the image. Moreover, the computation complexity of the iterative loop is high. In this paper we improve the technique using shuffling to equalize the capacities of different regions. Also, the iterative loop is replaced by a direct approach to reduce the computational complexity. We call this direct approach Direct JPEG Watermarking Embedding (DJWE). A Human Visual System (HVS) model is used to prioritize the DCT coefficients to achieve good visual quality. Experimental results show that the watermarks are robust against Gaussian noise attack and JPEG transcoding attacks.

title: Predictive motion estimation for reduced-resolution video from high-resolution compressed video
abstract: To convert a compressed video sequence to a lower-resolution compressed video, one typically needs to decompress the original sequence, down-sample each frame, and recompress it. It involves motion estimation in the reduced sequence which is computational intensive. We propose a novel fast algorithm to predict the motion vector of the reduced video by using the original motion information in the compressed bitstream. We achieve a much higher quality than existing algorithms with low additional complexity.

title: A blind watermarking technique for multiple watermarks
abstract: We propose a blind watermarking technique to embed multiple watermarks simultaneously. It allows the use of correlated key to embed multiple watermarks. A dual-key system is used to reduce the chance of the removal of watermark. Each embedded watermark can be decoded/detected by its own key. One direct approach and two iterative approaches are proposed to reduce the correlation effects among the noise-like keys. Experimental results show that multiple watermarks can be embedded without significant loss in PSNR and the watermark can be detected when the watermarked image is JPEG compressed.

title: Fast multi-block selection for H.264 video coding
abstract: The JVT/H.264 is the new video coding standard which achieves higher coding efficiency than previous standards such as H.263 or MPEG-2. The improvement in the rate-distortion performance in H.264 is mainly due to accurate motion estimation with different mode decision including multiple block size and multiple reference frame motion estimation. However, full exhaustive search of all block sizes is computational intensive with complexity increasing linearly with the number of allowed block size. In this paper, a fast multi-block motion estimation (FMBME) is proposed for H.264 video coding. Experimental results show that the proposed FMBME can efficiently reduce the computational cost by 58% with similar visual quality and bit rate.

title: A blind watermarking technique in JPEG compressed domain
abstract: We propose a blind watermarking technique to embed a watermark in the JPEG compressed domain. Low frequency DCT coefficients are extracted to form an M-dimensional vector. Watermarking is achieved by modifying this vector in order to point to the centroid of a particular cell. This cell is determined according to the extracted vector, private keys and the watermark. A dual-key system is used to reduce the chance of the removal of watermark. An iterative approach is used to prevent the removal of the watermark by JPEG re-quantization. Experimental results show that the watermark can be detected when the watermarked image is further compressed using a larger scaling factor.

title: Global Visualization and Alignments of Whole Bacterial Genomes
abstract: We present a novel visualization technique to align whole bacterial genomes with millions of nucleotides. Our basic design combines the descriptive power of pixel-based visualizations with the interpretative strength of digital image-processing filters. The innovative use of pixel enhancement techniques on pixel-based visualizations brings out the best of the recursive data patterns and further enhances the effectiveness of the visualization techniques. The result is a fast, versatile, and cost-effective analysis tool to reveal hidden structures that might lead to the discovery of functional identifications as well as phenotypic changes of whole bacterial genomes. Nine different whole bacterial genomes obtained from public genome banks are used to demonstrate our designs and prove their viability. Although the design of the new visualization technique is targeted at analyzing genomic sequences, we show with examples that it can be used to study other types of sequential data sets with a priori orders.

title: Discovering Knowledge Through Visual Analysis
abstract: This paper describes a vision for the near future in digital content analysis as it relates to the creation, verification, and presentation of knowledge. This paper focuses on how visualization enables humans to gain knowledge. Visualization, in this context, is not just the picture representing the data, but a two-way interaction between the human and their information resources for the purposes of knowledge discovery, verification, and the sharing of knowledge with others. We present PNNL-developed software to demonstrate how current technology can use visualization tools to analyze large diverse collections of text. This will be followed by lessons learned and the presentation of a core concept for a new human information discourse.

title: Graph Signatures for Visual Analytics
abstract: We present a visual analytics technique to explore graphs using the concept of a data signature. A data signature, in our context, is a multidimensional vector that captures the local topology information surrounding each graph node. Signature vectors extracted from a graph are projected onto a low-dimensional scatterplot through the use of scaling. The resultant scatterplot, which reflects the similarities of the vectors, allows analysts to examine the graph structures and their corresponding real-life interpretations through repeated use of brushing and linking between the two visualizations. The interpretation of the graph structures is based on the outcomes of multiple participatory analysis sessions with intelligence analysts conducted by the authors at the Pacific Northwest National Laboratory. The paper first uses three public domain data sets with either well-known or obvious features to explain the rationale of our design and illustrate its results. More advanced examples are then used in a customized usability study to evaluate the effectiveness and efficiency of our approach. The study results reveal not only the limitations and weaknesses of the traditional approach based solely on graph visualization, but also the advantages and strengths of our signature-guided approach presented in the paper

title: Performance Analysis of a Pull-Based Parallel Video Server
abstract: In conventional video-on-demand systems, video data are stored in a video server for delivery to multiple receivers over a communications network. The video server's hardware limits the maximum storage capacity as well as the maximum number of video sessions that can concurrently be delivered. Clearly, these limits will eventually be exceeded by the growing need for better video quality and larger user population. This paper studies a parallel video server architecture that exploits server parallelism to achieve incremental scalability. First, unlike data partition and replication, the architecture employs data striping at the server level to achieve fine-grain load balancing across multiple servers. Second, a client-pull service model is employed to eliminate the need for interserver synchronization. Third, an admission-scheduling algorithm is proposed to further control the instantaneous load at each server so that linear scalability can be achieved. This paper analyzes the performance of the architecture by deriving bounds for server service delay, client buffer requirement, prefetch delay, and scheduling delay. These performance metrics and design tradeoffs are further evaluated using numerical examples. Our results show that the proposed parallel video server architecture can be linearly scaled up to more concurrent users simply by adding more servers and redistributing the video data among the servers.

title: Visual Analytics
abstract: The information revolution is upon us, and it is guaran-teed to change our lives and the way we conduct our daily business. The fact that we have to deal with not just the size but also the variety and complexity of this in-formation makes it a real challenge to survive the revolu-tion. Enter Visual Analytics, a contemporary and proven approach to combine the art of human intuition and the science of mathematical deduction to directly perceive patterns and derive knowledge and insight from them.