Nonverbal cues in text-based computer-mediated communication (CMC), initially introduced to compensate for the lack of social and emotional cues, have evolved beyond their original purpose to express user identity. In particular, embodied identity cues—such as a user’s real face—remain relatively underexplored in text-based CMC despite their potential as richer cues. Recent advances in generative AI have lowered the barrier to AI-mediated self-presentation, yet empirical research is still needed to understand how these cues operate in real interactions and how users experience and accept them. To address this gap, we investigate the social and emotional effects of face-swapped GIFs (FSGIFs) created via generative AI. In a two-phase within-subjects experiment with 32 participants (16 dyads of close acquaintances), we find that FSGIFs significantly enhance relational benefits, including greater co-presence and intimacy compared to generic GIFs. Based on these findings and insights from interviews, we discuss design implications for AI-mediated self-presentation in text-based CMC.

Web agents aim to execute complex online tasks from high-level instructions, yet fully autonomous execution remains challenging in practice. We present an empirical study of user interventions in human–web agent collaboration, moving beyond outcome-based metrics to examine how interventions unfold during execution. We conducted a controlled in-lab study with 30 participants whose interactions reflected early-stage web agent adoption across 12 structured tasks in shopping, travel, and information-seeking domains using live websites. Analyzing interaction logs, user inputs, and screen recordings, we identify diverse behaviors and propose a taxonomy capturing both the reasons for intervention and the forms they take. We distinguish explicit interventions, where users halt or override actions, from implicit interventions, where users guide or prepare the environment without stopping execution. Our findings reveal how task structure and execution breakdowns shape intervention behaviors to provide process-level evidence for designing web agents that better support users as active collaborators.

Abstract Designers often create visualizations to achieve specific high‐level analytical or communication goals. These goals require people to extract complex and interconnected data patterns. Prior perceptual studies of visualization effectiveness have focused on low‐level tasks, such as estimating statistical quantities, and have recently explored high‐level comprehension of visualization. Despite the growing use of Large Language Models (LLMs) as visualization interpreters, how their interpretations relate to human understanding or what reasoning processes underlie their responses remains insufficiently understood. In this work, we explore LLMs' comprehension of visualization, examining the alignment between designers' communicative goals and what their audience sees. We have conducted a qualitative study to investigate the gap between human interpretative strategies and the reasoning pathways of LLMs across three types of visualizations, line graphs, bar graphs, and scatterplots, to identify the high‐level patterns generated by LLMs using three prompt conditions. Our analysis results indicate that LLMs exhibit a consistent interpretative strategy that remains unchanged across prompt constraints. Furthermore, we observe two distinct approaches: humans naturally synthesize data into trend‐centric narratives, whereas LLMs persist with a structural enumeration of comparisons and numerical ranges. Lastly, we see LLMs achieve visualization comprehension through mechanisms distinct from human intuition, pointing to critical challenges and new opportunities for visualization design.

Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs-graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. In this paper, we propose DG COMICS, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we provide an example and report the results of a user study and an expert review.

Few-shot object detection, which focuses on detecting novel objects with few labels, is an emerging challenge in the community. Recent studies show that adapting a pre-trained model or modified loss function can improve performance. In this paper, we explore leveraging the power of Contrastive Language-Image Pre-training (CLIP) and hard negative classification loss in low data setting. Specifically, we propose Re-scoring using Image-language Similarity for Few-shot object detection (RISF) which extends Faster R-CNN by introducing Calibration Module using CLIP (CM-CLIP) and Background Negative Re-scale Loss (BNRL). The former adapts CLIP, which performs zero-shot classification, to re-score the classification scores of a detector using image-class similarities, the latter is modified classification loss considering the punishment for fake backgrounds as well as confusing categories on a generalized few-shot object detection dataset. Extensive experiments on MS-COCO and PASCAL VOC show that the proposed RISF substantially outperforms the state-of-the-art approaches. The code will be available.

As online news increasingly include data journalism, there is a corresponding increase in the incorporation of visualization in article thumbnail images. However, little research exists on the design rationale for visualization thumbnails, such as resizing, cropping, simplifying, and embellishing charts that appear within the body of the associated article. Therefore, in this paper we aim to understand these design choices and determine what makes a visualization thumbnail inviting and interpretable. To this end, we first survey visualization thumbnails collected online and discuss visualization thumbnail practices with data journalists and news graphics designers. Based on the survey and discussion results, we then define a design space for visualization thumbnails and conduct a user study with four types of visualization thumbnails derived from the design space. The study results indicate that different chart components play different roles in attracting reader attention and enhancing reader understandability of the visualization thumbnails. We also find various thumbnail design strategies for effectively combining the charts' components, such as a data summary with highlights and data labels, and a visual legend with text labels and Human Recognizable Objects (HROs), into thumbnails. Ultimately, we distill our findings into design implications that allow effective visualization thumbnail designs for data-rich news articles. Our work can thus be seen as a first step toward providing structured guidance on how to design compelling thumbnails for data stories.

We present Roslingifier, a data-driven storytelling method for animated scatterplots. Like its namesake, Hans Rosling (1948-2017), a professor of public health and a spellbinding public speaker, Roslingifier turns a sequence of entities changing over time-such as countries and continents with their demographic data-into an engaging narrative elling the story of the data. This data-driven storytelling method with an in-person presenter is a new genre of storytelling technique and has never been studied before. In this article, we aim to define a design space for this new genre-data presentation-and provide a semi-automated authoring tool for helping presenters create quality presentations. From an in-depth analysis of video clips of presentations using interactive visualizations, we derive three specific techniques to achieve this: natural language narratives, visual effects that highlight events, and temporal branching that changes playback time of the animation. Our implementation of the Roslingifier method is capable of identifying and clustering significant movements, automatically generating visual highlighting and a narrative for playback, and enabling the user to customize. From two user studies, we show that Roslingifier allows users to effectively create engaging data stories and the system features help both presenters and viewers find diverse insights.

Music creation is difficult because one must express one's creativity while following strict rules. The advancement of deep learning technologies has diversified the methods to automate complex processes and express creativity in music composition. However, prior research has not paid much attention to exploring the audiences' subjective satisfaction to improve music generation models. In this paper, we evaluate human satisfaction with the state-of-the-art automatic symbolic music generation models using deep learning. In doing so, we define a taxonomy for music generation models and suggest nine subjective evaluation metrics. Through an evaluation study, we obtained more than 700 evaluations from 100 participants, using the suggested metrics. Our evaluation study reveals that the token representation method and models' characteristics affect subjective satisfaction. Through our qualitative analysis, we deepen our understanding of AI-generated music and suggested evaluation metrics. Lastly, we present lessons learned and discuss future research directions of deep learning models for music creation.

As online marketplaces adopt new technologies to encourage consumers’ purchases (e.g., one-click purchases), the number of consumers who impulsively buy products also increases. Although some interventions have been introduced for consumers’ self-controlled purchases, there have been few studies that evaluate the effectiveness of the techniques in the real environment. In this paper, we conducted an online survey with 118 consumers in their 20s to investigate their impulse buying behaviors and self-control strategies. Based on the survey results and literature surveys, we developed interventions that can assist consumers in controlling their online purchase habits, including Reflection, Distraction, Desire Reduction, and Salient Cost. For evaluation, we enrolled 107 participants in a user study on a real-world e-commerce site. The results indicate that all interventions were effective in reducing impulse buying urges, with variations in user experiences. Our findings and design implications are discussed.

Multi-class and multi-scale object detection for autonomous driving is challenging because of the high variation in object scales and the cluttered background in complex street scenes. Context information and high-resolution features are the keys to achieve a good performance in multi-scale object detection. However, context information is typically unevenly distributed, and the high-resolution feature map also contains distractive low-level features. In this paper, we propose a location-aware deformable convolution and a backward attention filtering to improve the detection performance. The location-aware deformable convolution extracts the unevenly distributed context features by sampling the input from where informative context exists. Different from the original deformable convolution, the proposed method applies an individual convolutional layer on each input sampling grid location to obtain a wide and unique receptive field for a better offset estimation. Meanwhile, the backward attention filtering module filters the high-resolution feature map by highlighting the informative features and suppressing the distractive features using the semantic features from the deep layers. Extensive experiments are conducted on the KITTI object detection and PASCAL VOC 2007 datasets. The proposed method shows an average 6% performance improvement over the Faster R-CNN baseline, and it has the top-3 performance on the KITTI leaderboard with the fastest processing speed.

Video object detection enhances the performance of the still-image based object detection by exploiting temporal context information from neighboring frames. Most of the state-of-the-art video object detectors are non-causal and require lots of preceding and succeeding frames, which makes them impractical for real-time online detection where succeeding frames are not available. In this paper, we propose a causal recurrent flow-based method for online video object detection. The proposed method reads only the current frame and one preceding frame from memory buffer at each time step. Two types of temporal context information are utilized. The short-term temporal context information is utilized by warping the feature map from the nearby preceding frame based on the optical flow. The long-term temporal context information is extracted from the temporal convolutional LSTM, where informative features from distant preceding frames are stored and propagated through the time. By aggregating both long- and short-term temporal context information, our proposed method achieves competitive performance (75.5% mAP) on ImageNet VID dataset at a relatively high speed.

Real-time 3D reconstruction using low-cost commodity sensors like Kinect or Xtion has been successfully applied in a wide range of fields like augmented reality, robotic teleoperation, and medical diagnosis. Due to the assumption of static scene, popular 3D reconstruction technologies such as KinectFusion and KinFu, find truthful reconstruction with fast motion camera or segmenting a moving object to be a challenge. In this paper, we propose a weighted iterative closest point (ICP) algorithm that uses both depth and RGB information to enhance the stability of camera tracking. Additionally, a GPU-based region growing method that combines depth, normal and intensity level as similarity criteria, is also applied to segment foreground moving objects accurately. For real-time processing and GPU memory efficiency, we also design a combination of point-based and volumetric representation to reconstruct moving objects and static scene, respectively. Both qualitative and quantitative results show that our proposed method improves real-time 3D reconstruction on the performance of camera tracking and segmentation of moving objects with reduced computational complexity.

Compressed sensing (CS) is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Exact reconstruction may then be possible with much fewer than the Nyquist-required number of data. In this paper, we consider a multiview video system in which multiple cameras at different locations perform independent CS to simultaneously capture different views of a scene. At the decoder, we propose a disparity- and motion-compensated total variation minimization algorithm to jointly reconstruct the multiview video sequence. The experimental results show that the proposed joint reconstruction algorithm successfully exploits simultaneously intra-frame, inter-frame, and inter-view sparsity and significantly outperforms existing independent-view reconstruction, residue-view reconstruction, and motion-adaptive reconstruction algorithms.

Region of interest (ROI) generation is an important step in stereo-based pedestrian detection systems. In this paper, we propose an ROI generation method by fusing the color and depth information obtained from a stereo camera mounted on a vehicle. In our proposed method, a feature-based method which uses contour properties of the image is used to find the ROIs. In our feature-based ROI extraction method, we extract four features which are contour density, maximum area, maximum perimeter and matching score. Then we create a feature vector from these features and classify them using SVM. ROIs are then classified into the pedestrian and non-pedestrian classes using Histogram of Oriented Gradients (HOG)/Linear SVM. We have tested our proposed method on the Daimler dataset and experimental results show that our proposed method has a 96.5% accuracy for 1 false positive per frame and outperforms existing monocular and stereo-based methods.

As one of the most successful features in vision-based pedestrian detection, filtered channel features have drawn considerable attention in the research community. In this paper, we improve the channel features by performing a statistical analysis of each channel and taking both the average map and variance map into account. The average map informs us the local structure of the human body parts and how it looks like in general. The variance map points out the region where high variation in human poses takes place. Based on both the average and variance information, we create two types of templates, each employs a different design strategy and generates the feature value in different ways. We also utilize the co-existence of strong responses that take place in non-neighboring region pairs to enrich the feature pool. Experimental results show that the proposed method achieves good performance on the INRIA and Caltech-USA benchmark with small feature size, short training time, and less training examples are required.

Compressed sensing (CS) is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Perfect reconstruction may then be possible with much fewer than the Nyquist required number of data. In this paper, we consider a distributed multi-view imaging system where each camera at a different location performs independent compressed sensing acquisition of the target scene. At the decoder, we propose a disparity-compensated total-variation (TV) minimization algorithm to jointly reconstruct the multiple views. Experimental results show that the proposed joint decoding algorithm outperforms significantly independent-view decoding as well as disparity-compensated residue-view reconstruction algorithm.

Region of interest (ROI) generation used to extract candidate windows which are likely to have pedestrians is an important step in pedestrian detection systems. In this paper, we propose a novel algorithm to reduce the number of candidate windows extracted as ROI to reduce the computation time of pedestrian detection. In our proposed method, we take advantage of the temporal correlation between the adjacent frames to discard the candidate windows in regions where pedestrians cannot be found. To do this, we compare the confidence scores computed for the ROIs in the previous frames in two steps and ignore the regions where all of the neighboring ROIs are classified as non-pedestrian. Simulation results show that our proposed algorithm can improve the speed of pedestrian detection significantly compared to the existing systems while maintaining comparable accuracy.

Real-time 3D reconstruction using depth information has become a critical part of many emerging applications, like augmented reality, autonomous robot navigation and teleoperation to hazardous locations. This paper presents two novel methods to improve on the 3D reconstruction quality of static scenes using depth images. First, a new anisotropic depth filtering along with confidence indicator for every filtered depth value is proposed. Second, a modified pose estimation method, which incorporates the uncertainty or error characteristics of the depth measured as part of the Iterative Closet Point (ICP) algorithm is proposed. Both these methods are combined together along with a pre-existing confidence indicator based 3D reconstruction method, to get a robust and real-time 3D reconstruction framework. Simulations results show that this combined algorithm gives 59.60% quantitative improvement in pose estimation and achieves a significant quality improvement in 3D reconstruction compared to the baseline algorithm.

Generating a high-quality disparity map is a fundamental step in many applications such as stereo vision-based pedestrian detection for advanced driver assistance systems (ADAS). One of the major challenges in generating accurate depth maps is the huge computational complexity in stereo matching. In this paper, we propose a fast depth estimation technique for real-time applications. The proposed architecture employs spatial and temporal disparity prediction modules in order to decrease spatio-temporal redundancy. In order to evaluate the performance of the proposed method systematically, we apply the generated depth maps to a stereo-based pedestrian detection system. Simulation results show that the proposed method reduces the computational complexity by 68%-83% while maintaining comparable detection performance with the full-search block matching algorithm used as a reference.

We consider a variable block size compressed sensing (CS) framework for high efficiency depth map coding. In this context, quad-tree decomposition is performed on a depth image to differentiate irregular uniform and edge areas prior to CS acquisition. To exploit temporal correlation and enhance coding efficiency, such quad-tree based CS acquisition is further extended to inter-frame encoding, where block partitioning is performed independently on the I frame and each of the subsequent residual frames. At the decoder, pixel domain total-variation minimization is performed for high quality depth map reconstruction. Experiments presented herein illustrate and support these developments.

In recent years, compressed sensing (CS) has been used for compressing depth maps for the multi-view video plus depth. In these compression schemes, every block of the depth map is sampled with a fixed non-adaptive sensing matrix and the algorithms are generally incorporated into conventional codecs like H.264/AVC, resulting in high computational complexity both at the encoder and decoder. In this paper, we present a novel block-based CS codec for compressing depth maps that has two major features. First, an adaptive measurement rate allocation algorithm is introduced that computes the measurement rate for each compressively sensed block using ratedistortion optimization (RDO). Second, a simple block classification and frame-differencing module is utilized to reduce encoding complexity while maintaining good RD performance. Simulation results clearly show that the proposed codec has superior rate-distortion (RD) performance in comparison to H.264/AVC Baseline Profile (up to 3 dB gain) and an encoding time reduction of up to 97%.

In order to guarantee signal integrity of a TSV-based channel in 3D IC design, the modeling and measurements are conducted for electrical characterization of the TSV-based channel including TSVs and RDLs with various performance metrics such as insertion loss, noise coupling and eye diagrams. Based on the modeling and measurements of the fabricated TSV channels, design guide for the signal integrity of the channel is proposed.

Pedestrian detection is one of the major goals in advanced driver assistance systems (ADAS) which has become an active research area in recent years. In this paper, we present a stereo based pedestrian detection system by fusing the depth and color data provided by a stereo vision camera on a moving platform. The proposed method uses an adaptive window for region of interest (ROI) generation using dense depth map. The extracted candidates are then applied to a Histogram of Oriented Gradients (HOG) feature descriptor to refine ROIs and Support Vector Machine (SVM) is used to classify them into pedestrian and non-pedestrian classes. The system is tested on a stereo based DAS dataset and results show that our system is able to detect pedestrians with different scales and illumination conditions and in presence of partial occlusion.

Depth-image based rendering is a popular technique for rendering virtual views for the multi-view video plus depth (MVD) 3DTV format. Instead of transmitting a high number of views to the receiver, only a few views are transmitted along with their corresponding depth maps and the missing views are interpolated from these transmitted views. This process is called view synthesis and has been the focus of a lot of research in recent times. In addition to the obvious quality constraints of view synthesis, focus needs to be laid on the efficiency and speed of the view synthesis process. In this paper, we propose a simple, highly efficient view synthesis process that utilizes a separable look-up table based technique to speed up the 3D warping process. We also develop a reliability-based weighting scheme for blending the candidate views into the final synthesized view. Simulation results show that the proposed method is approximately 6 times faster than VSRS 3.5 and has comparable objective quality. The proposed method is inherently parallel thus making it suitable to act as a framework for implementing high-speed view synthesis on a GP-GPU.

This paper investigates the use of segmentation in view synthesis for three-dimensional video. View synthesis is the process of generating novel views of a scene, using a set of views as the reference. Recently, several techniques that use depth maps for rendering virtual views have been suggested. However, inaccuracy in depth maps causes annoying visual artifacts in depth-based view synthesis. This paper presents an efficient depth image-based rendering technique based on segmentation using multi-level thresholding. In the proposed algorithm, first all the images are segmented according to the depth and the pixels belonging to different objects are warped and blended independently. Based on multi-level thresholding, an algorithm for finding the ghost contour pixels is provided which simplifies the computations. A novel inpainting method for disocclusions has been introduced which uses the segmented images to find the associated background boundary pixels. The experimental results show that the proposed algorithm improves the PSNR of the synthesized views up to 0.68 dB for the multi-view video test sequences and eliminates the annoying visual artifacts.

Space-time feature extraction is a recent and popular method used for action recognition. This paper presents a new algorithm to improve the robustness of spatio-temporal feature extraction techniques against the illumination and scale variations. Most of the interest point detectors are sensitive to illumination variations that may cause serious problems in action recognition by finding wrong keypoints. A method is proposed to make the 3-D Harris corner detector robust to illumination changes. Illumination invariance is achieved by applying a contrast stretching function to the video to find the proper intensity level for each pixel. A non-uniform binning method is also proposed to make the 3-D extension of the well-known SIFT descriptor more reliable and robust to scale changes by forming orientation histograms which concentrate on the regions near the interest points. Bag of features technique is used for classifying actions provided by the KTH dataset and the results demonstrate that our proposed method outperforms the original 3-D corner detector and SIFT descriptor.

This paper studies TSV-to-TSV coupling in 3D ICs. A full-chip SI analysis flow is proposed based on the proposed coupling model. Analysis results show that TSVs cause significant coupling noise and timing problems despite that TSV count is much smaller compared with the gate count. Two approaches are proposed to alleviate TSV-to-TSV coupling, namely TSV shielding and buffer insertion. Analysis results show that both approaches are effective in reducing the TSV-caused-coupling and improving timing.

Distributed video coding (DVC) is a video coding paradigm that aims at shifting the complexity from the encoder to the decoder. Most encoding schemes in DVC use a GOP size of 2 or 3 which is fixed and doesn't depend on the video characteristics. In this paper, we propose an architecture that has a unique and simple GOP size control module that results in greater rate-distortion (RD) performance. The proposed codec also has an efficient two-phase block classification scheme and a prioritized block classification scheme to further improve the efficiency. It also uses arithmetic coding instead of the iterative turbo or low density parity check accumulate (LD-PCA) codes used in most other DVC codecs, thus reducing system latency tremendously. Results show that the proposed codec outperforms a GOP size control algorithm from literature with a gain of almost 4dB at similar bitrates.

In this paper, a low-delay distributed multiple description coding (LD-DMDC) method that combines the principles of multiple description coding (MDC) and distributed video coding (DVC) has been proposed to further improve the error resilience of DVC. The proposed method generates two descriptions based on duplication and alternation of the discrete cosine transform (DCT) coefficients for the Wyner-Ziv (WZ) frames and by exploiting H.264/AVC's dispersed flexible macroblock ordering (FMO) for the key frames. The proposed method makes efficient use of skip blocks to exploit temporal redundancies between successive frames and employs a binary arithmetic coding instead of iterative channel coding to reduce the system latency. Simulation results show that the proposed method is robust against transmission errors, while maintaining low encoder complexity and low system latency.

Distributed video coding is a new paradigm based on two information theoretical results by Slepian-Wolf and Wyner-Ziv. The architectures designed so far have invariably made use of the uniform scalar quantization schemes along with a few attempts to make the schemes more adaptive. Quantization is one of the major contributors to the large performance gap between conventional video coding standards and distributed video coding. In this paper, an attempt is made to improve the performance of the Wyner-Ziv video coding by making the quantization algorithm more adaptive to the motion content of the video sequence without significantly increasing the encoder complexity. The proposed method also exploits the temporal correlation to provide for online correlation noise classification. Hence, the improved reconstruction technique which uses the correlation noise information is more adaptive to the motion content. Simulation results show that the proposed motion-adaptive quantization and reconstruction technique achieves improved rate-distortion performance.

Smart spaces is a scenario that is ideal for the application of wireless communication technology to benefit non-expert users. A prime example of such an application would be low-cost and real-time video surveillance of hallways or video conferencing. Traditional solutions for these scenarios use state-of-the-art video codecs like H.264/AVC in order to achieve the low bitrate which is essential in wireless communication. On the flip side, such encoders are computationally complex and need expensive hardware to run on thus making them inefficient choices for being deployed in clusters. Recent research has resulted in distributed video coding (DVC) being proposed as a solution for applications that have limited battery resources and low hardware complexity, thus necessitating a low-complexity encoder. It is now a popular topic in the research community and the past years have seen several implementations. However, current DVC solutions use iteratively decodable channel codes like low-density parity check – accumulate (LDPCA) codes or Turbo codes that have large latencies. In order to make real-time communication possible, the proposed architecture makes efficient use of skip blocks to reduce the bitrate, eliminates the iterative decoding nature of the Wyner-Ziv (WZ) channel and uses a simple data-hiding-based compression algorithm. This drastically cuts down on the time complexity of the decoding procedure while still maintaining a rate–distortion performance better than that of H.264/AVC intra-coding and other current DVC solutions.

Distributed video coding is a new paradigm for video compression based on the Slepian-Wolf and Wyner-Ziv theorems. Wyner-Ziv video coding, a lossy compression with receiver side information, enables low-complexity video encoding at the expense of a complex decoder. Most of the existing distributed video coding techniques require a feedback channel to determine the number of parity bits for decoding Wyner-Ziv frames at the decoder. However, a feedback channel is not available for some applications or a feedback channel-based decoder rate control may not be used due to delay constraints in wireless video sensor network applications. In this paper, an encoder-based rate control method for distributed video coding is proposed. The proposed solution consists of a low complexity side information generation method at the encoder and a rate estimation algorithm that determines the number of parity bits to be transmitted to the decoder. The performance of the proposed algorithm is compared with existing encoder-based rate control methods and a decoder-based rate control algorithm based on a feedback channel.

Distributed Video Coding (DVC) is a popular topic in the research community and the past years have seen several different implementations. DVC has been proposed as a solution for applications that have limited battery resources and low hardware complexity, thus necessitating a low complexity encoder. An ideal application would be in remote surveillance/monitoring or live video conferencing. However, current solutions use iteratively decodable channel codes like LDPCA or Turbo codes that have large latencies. In order to make real-time communication possible. The proposed architecture makes efficient use of Skip blocks to reduce the bitrate, eliminates the iterative decoding nature of the Wyner-Ziv (WZ) channel and uses a simple data-hiding based compression algorithm. This drastically cuts down on the time complexity of the decoding procedure while still maintaining an rate-distortion performance better than that of H.264/AVC Intra coding and other current DVC solutions.

This paper proposes a channel-adaptive multiple description coding (MDC) method for robust video delivery in wireless ad-hoc networks. In the proposed system, our multi-stream rate allocation algorithm determines the optimal source coding rates and the amount of redundancy in MDC under time-varying network conditions. In addition, our flexible MD coder generates two correlated descriptions from a single description coder using a redundancy-rate-distortion optimized coefficient splitting algorithm. Simulation results show that the proposed MD coding outperforms non-adaptive MD coding under various network conditions. The proposed video streaming system is error-resilient, network-adaptive, and provides a relatively constant video quality in time-varying networks, and therefore can be a promising solution for robust video communication in wireless ad-hoc networks.

This paper presents a distributed video streaming framework using unbalanced multiple description coding (MDC) and unequal error protection. In the proposed video streaming framework, two senders simultaneously stream complementary descriptions to a single receiver over different paths. To minimize the overall distortion and exploit the benefits of multipath transport when the characteristics of each path are different, an unbalanced MDC method for wavelet-based coders combined with a TCP-friendly rate allocation algorithm is proposed. The proposed rate allocation algorithm adjusts the transmission rates and the channel coding rates for all senders in a coordinated fashion to minimize the overall distortion. Simulation results show that the proposed unbalanced MDC combined with our rate allocation algorithm achieves about 1-6 dB higher peal signal-to-noise ratio compared to conventional balanced MDC when the available bandwidths along the two paths are different under time-varying network conditions.

This paper proposes a constant-load SRAM design for highly efficient recovery of bit-line energy with a resonant power-clock supply. For each bit-line pair, the proposed SRAM includes a dummy bit-line of sufficient capacitance to ensure that the memory array presents a constant capacitive load to the power-clock, regardless of data or operation. Using a single-phase power-clock waveform, read and write operations are performed with single-cycle latency. The efficiency of the proposed SRAM has been assessed through simulations of 128x256 arrays with 0.25µm process parameters and a 42/58 write/non-write access pattern. Assuming lossless power-clock generation, the proposed SRAM dissipates 37% less power than its conventional counterpart at 400MHz/2.5V. When the overhead of power-clock generation is included, the proposed SRAM dissipates at least 27% less power than conventional SRAM.

This paper presents a coordinated multiple-substream unequal error-protection and error-concealment algorithm for SPIHT-coded bitstreams transmitted over lossy channels. In the proposed scheme, we divide the video sequence corresponding to a group of pictures into two subsequences and independently encode each subsequence using a three-dimensional SPIHT algorithm. We use two different partitioning schemes to generate the substreams, each of which offers some advantages under the appropriate channel condition. Each substream is protected by an FEC-based unequal error-protection algorithm, which assigns unequal forward error correction codes to each bit plane. Any information that is lost during the transmission for any substream is estimated at the receiver by using the correlation between the substreams and the smoothness of the video signal. Simulation results show that the proposed multiple-substream UEP algorithm is simple, fast, and robust in hostile network conditions, and that the proposed error-concealment algorithm can achieve 2-3-dB PSNR gain over the case when error concealment is not used at high packet-loss rates.

This paper presents a distributed video streaming framework using unbalanced multiple description coding (MDC) and forward error correction. In the proposed video streaming framework, two senders simultaneously stream complementary descriptions to the receiver over different paths. To minimize the overall distortion and exploit the benefits of multi-path transport when the characteristics of each path are different, an unbalanced MDC method for wavelet-based coders combined with a TCP-friendly rate allocation algorithm is proposed. The proposed rate allocation algorithm adjusts the transmission rates and the channel coding rates for all senders in a coordinated fashion to minimize the overall distortion. Simulation results show that the proposed unbalanced MDC combined with our rate allocation algorithm achieves about 1-3 dB higher PSNR compared to conventional balanced MDC when the available bandwidth along each path is different in time-varying network conditions.

Layered (LC) and multiple description coding (MDC) have been proposed as source coding techniques that are robust to channel errors for video transmission. In this paper, we investigate and study the error-resilience capabilities of these two encoding techniques through extensive experimentation, and explore the environments where they perform best. Although some of our conclusions agree with those in the literature, we believe that this paper provides the most comprehensive performance comparison yet between LC and MDC.

This paper presents a TCP-friendly rate allocation algorithm for multiple description coding combined with path diversity. A 3-D SPIHT-based multiple description coding is used to generate two independent substreams and each substream is protected with an FEC-based unequal error protection algorithm. In the proposed video streaming framework, two senders simultaneously stream complementary substreams to the receiver over different paths. The proposed rate allocation algorithm coordinates distributed video streaming from multiple senders to a single receiver to minimize the overall distortion. Simulation results show that the proposed algorithm increases tolerance to packet loss due to network congestion and achieves higher visual quality compared to non-adaptive methods.

We have recently designed, fabricated, and successfully tested an experimental chip that validates a novel method for reducing clock dissipation through energy recovery. Our approach includes a single-phase sinusoidal clock signal, an L-C resonant sinusoidal clock generator, and an energy recovering flip-flop. Our chip comprises a dual-mode ASIC with two independent clock systems, one conventional and one energy recovering, and was fabricated in a micron bulk CMOS process. The ASIC computes a pipelined discrete wavelet transform with self-test and contains over 3500 gates. We have verified correct functionality and obtained power measurements in both modes of operation for frequencies up to 225MHz. In the energy recovering mode, our power measurements account for all of the dissipation factors, including the operation of the integrated resonant clock generator, and show a net energy savings over the conventional mode of operation. For example, at 115MHz, measured dissipation is between 60% and 75% of the conventional mode, depending on primary input activity. To our knowledge, this is the first ever published account of a direct experimentally-measured comparison between a complete energy recovering ASIC chip and its conventional implementation correctly operating in silicon at frequencies exceeding 100MHz.

This paper presents a new bit-plane-wise unequal error protection algorithm for progressive bitstreams transmitted over lossy networks. The proposed algorithm protects a compressed embedded bitstream generated by a 3-D SPIHT algorithm by assigning an unequal amount of forward error correction (FEC) to each bit-plane. The proposed algorithm reduces the amount of side information needed to send the size of each code to the decoder by limiting the number of quality levels to the number of bit-planes to be sent while providing a graceful degradation of picture quality as packet losses increase. We also apply our proposed algorithm to transmission of JPEG 2000 coded images over the Internet. To get additional error-resilience at high packet loss rates, we extend our algorithm to multiple-substream unequal error protection. Simulation results show that the proposed algorithm is simple, fast and robust in hostile network conditions and, therefore, can provide reasonable picture quality for video applications under varying network conditions.

Dynamic random access memorys (DRAMs) are widely used in portable applications due to their high storage density. In standby mode, the main source of DRAM power dissipation is the refresh operation that periodically restores leaking charge in each memory cell to its correct level. Conventional DRAMs use a single refresh period determined by the cell with the largest leakage. This approach is simple but dissipative, because it forces unnecessary refreshes for the majority of the cells with small leakage. In this paper, we investigate a novel scheme that relies on small refresh blocks and multiple refresh periods to reduce DRAM dissipation by decreasing the number of cells refreshed too often. In contrast to conventional row-based refresh, small refresh blocks are used to increase worst case data retention times. Long periods are used to accommodate cells with small leakage. Retention times are further extended by adding a swap cell to each refresh block. We give a novel polynomial-time algorithm for computing an optimal set of refresh periods for block-based multiperiod refresh. Specifically, given an integer K and a distribution of data-retention times, in O(KN/sup 2/) steps our algorithm computes K refresh periods that minimize DRAM dissipation, where N is the number of refresh blocks in the memory. We describe and evaluate a scalable implementation of our refresh scheme whose overhead is asymptotically linear with memory size. In simulations with a 16-Mb DRAM, block-based multiperiod refresh reduces DRAM standby dissipation by a multiplicative factor of 4 with area overhead below 6%. Moreover, our proposed scheme is robust to semiconductor process variations, with power savings degrading no more than 7% over a 20-fold increase of leaky cells.

This paper presents a new bit-plane-wise unequal error protection algorithm for Internet video applications. The proposed algorithm protects a compressed embedded bitstream generated by a 3-D SPIHT algorithm by assigning unequal forward error correction (FEC) codes to each bit-plane. The proposed algorithm reduces the amount of side information needed to send the size of each code to the decoder by limiting the number of quality levels to the number of bit-planes to be sent while providing a graceful degradation of picture quality as packet losses increase. To get additional error-resilience at high packet loss rates, we extend our algorithm to multiple-substream unequal error protection. Simulation results show that the proposed algorithm is simple, fast and robust in hostile network conditions and therefore can provide reasonable picture quality for Internet video applications under varying network conditions.

This paper presents a coordinated multiple-substream unequal error protection and error concealment algorithm for SPIHT-coded bitstreams transmitted over lossy channels. In the proposed scheme, we divide the video sequence corresponding to a group of pictures into two sub-sequences in the temporal domain and independently encode each sub-sequence with a 3-D SPIHT algorithm to generate two independent substreams. Each substream is protected by an FEC-based unequal error protection algorithm that assigns unequal forward error correction codes for each substream with bit-plane granularity. The information that is lost during transmission for one substream is estimated at the receiver by using the correlation between the two substreams and the smoothness of the video signal. Simulation results show that the proposed multiple-substream UEP algorithm is simple, fast, and robust in hostile network conditions, and that the proposed error concealment algorithm achieves about 1-3 dB PSNR gain over the case where there is no error concealment at high packet loss rates.

This paper proposes an energy-recovering (a.k.a. adiabatic) static RAM with a novel driver that reduces power dissipation by efficiently recovering energy from the bit/word line capacitors. Powered by a single-phase sinusoidal power-clock, our SRAM delivers read and write operations with single-cycle latency. To that end, a precharge-low scheme is employed along with a modified sense amplifier design that achieves high efficiency at differential voltages near V/sub SS/. A simple control circuit is used to maintain driver operation in synchrony with the power-clock waveform. Feedback circuitry from the driver output to the control circuit ensures that out driver remains efficient, independent of the access pattern. Our energy recovering SRAM functions correctly while achieving substantial energy savings over a wide range of supply voltages and operating frequencies. Hspice simulations of a simple full-custom adiabatic 256/spl times/256 SRAM, that includes the energy recovering bit/word line drivers, the cell array, and the sense amplifiers, show over 2.6x energy savings at 3V, 300MHz in comparison with its conventional counterpart.