A blind watermarking technique in JPEG compressed domain
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: 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: Novel blind multiple watermarking technique for images
abstract: Three novel blind watermarking techniques are proposed to embed watermarks into digital images for different purposes. The watermarks are designed to be decoded or detected without the original images. The first one, called single watermark embedding (SWE), is used to embed a watermark bit sequence into digital images using two secret keys. The second technique, called multiple watermark embedding (MWE), extends SWE to embed multiple watermarks simultaneously in the same watermark space while minimizing the watermark (distortion) energy. The third technique, called iterative watermark embedding (IWE), embeds watermarks into JPEG-compressed images. The iterative approach of IWE can prevent the potential removal of a watermark in the JPEG recompression process. Experimental results show that embedded watermarks using the proposed techniques can give good image quality and are robust in varying degree to JPEG compression, low-pass filtering, noise contamination, and print-and-scan.

title: Hybrid inverse halftoning using adaptive filtering
abstract: We propose a novel fast inverse halftoning technique using a combination of spatial varying filtering and spatial invariant filtering. The proposed algorithm is significantly simpler than most existing algorithms. Without explicit edge region classification, the proposed spatial varying filter is shown to be capable of preserving edges effectively.

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: 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: Dynamic Visualization of Graphs with Extended Labels

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: 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.

title: Multivariate Visualization with Data Fusion
abstract: We discuss a fusion-based visualization method to analyze a multivariate climate dataset and its metadata. The primary difference between a conventional visualization and a fusion-based visualization is that the former draws on a single image whereas the latter draws on multiple see-through layers, which are then overlaid on each other to form the final visualization. We propose optimized colormaps to highlight subtle features that would not be shown with conventional colormaps. We present fusion techniques that integrate multiple single-purpose visualization techniques into the same viewing space. Our highly flexible fusion approach allows scientists to explore multiple parameters concurrently by mixing and matching images without frequently reconstructing new visualizations from the data for every possible combination. Although our primary visualization application is climate modeling, we show with examples that our fundamental design - fusing layers of data images for multivariate visualization - can be generalized for other information visualization applications.