nils-herrmann/clean_scirepeval huggingface dataset:
Single-pass distortion-smoothing encoding for low bit-rate video streaming applications

This paper proposes a rate control scheme for smoothing distortion in low bit-rate video coding. We focus on single-pass encoding that is applicable to both live and off-line streaming. The new rate control tends to achieve slow and smooth distortion variation over time. Without the knowledge of future frames, statistics of previously coded frames are used to derive the expected distortion for the current frame. Constraints of decoder buffer size and pre-loading time are considered in the design. The proposed technique is based on the rate and distortion models used in the TMN8. Its advantages have been shown by experiments.
A novel incremental rate control scheme for H.264 video coding

In this paper, we propose a new incremental rate control scheme for H.264/AVC video coding, which elegantly resolves the "Chicken and Egg" dilemma by eliminating the need of coding complexity prediction for inter-frames. The proposed scheme introduces a number of new features, including a rate- complexity-quantization model, accurate quantization parameter (QP) estimation for intra-frames, incremental QP calculation for inter-frames, and a basic unit level QP adjustment. Experimental results demonstrate that the proposed scheme outperforms the JVT-G012 solution by providing more accurate rate control, reducing frame skipping, and decreasing quality fluctuations, and finally, improving coding quality by up to 1.85 dB.
Distributed Optimization Over Wireless Sensor Networks using Swarm Intelligence

In this work, we study how a group of sensor nodes in a wireless sensor network could collaborate with each other to perform complicated signal processing (e.g. estimation and tracking) and optimization tasks through local communication and distributed computation. We develop a distributed evolutionary optimization framework based on a swarm intelligence principle. During the optimization process, we only use and share local estimation results through communication links. This scheme can significantly reduce the communication energy cost and reach fast convergence. We use target localization as an example to evaluate the performance of the proposed distributed optimization algorithm. Our simulation results demonstrate that it outperforms existing distributed optimization algorithms, such as distributed gradient search.
Peak Transform - A Nonlinear Transform for Efficient Image Representation and Coding

In this work, we introduce a nonlinear geometric transform, called peak transform, for efficient image representation and coding. Coupled with wavelet transform and subband decomposition, the peak transform is able to significantly reduce signal energy in high-frequency subbands and achieve a significant transform coding gain. This has important applications in efficient data representation and compression. Based on peak transform (PT), we design an image encoder, called PT encoder, for efficient image compression. Our extensive experimental results demonstrate that, in wavelet-based subband decomposition, the signal energy in high-frequency subbands can be reduced by up to 60% if a peak transform is applied. The PT image encoder outperforms state-of-the-art JPEG2000 and H.264 (INTRA) encoders by up to 2-3 dB in PSNR (peak signal-to-noise ratio), especially for images with a significant amount of high-frequency components.
Doubling of the Operational Lifetime of Portable Video Communication Devices Using Power-Rate-Distortion Analysis and Control

We consider a portable video device which captures and compresses video data. Video compression is computationally intensive and energy-consuming. However, the portable device, powered with batteries, has limited energy supply for data processing. One of the central challenging issues in portable video communication system design is how to maximize the operational lifetime. Based on our previous work on power-rate-distortion (P-R-D) analysis, in this work, we study how we could trade "bits" for "jouls" (energy) to minimize the energy consumption of video compression using optimum bit allocation. Our results, both theoretically and experimentally, show that using the proposed P-R-D video encoding technology, the operational lifetime of the portable device can be doubled or even tripled. This has a significant impact in energy-efficient portable video communication system design.
Fine-Granularity Transmission Distortion Modeling for Video Packet Scheduling Over Mesh Networks

Packet scheduling is a critical component in multi-session video streaming over mesh networks. Different video packets have different levels of contribution to the overall video presentation quality at the receiver side. In this work, we develop a fine-granularity transmission distortion model for the encoder to predict the quality degradation of decoded videos caused by lost video packets. Based on this packet-level transmission distortion model, we propose a content-and-deadline-aware scheduling (CDAS) scheme for multi-session video streaming over multi-hop mesh networks, where content priority, queuing delays, and dynamic network transmission conditions are jointly considered for each video packet. Our extensive experimental results demonstrate that the proposed transmission distortion model and the CDAS scheme significantly improve the performance of multi-session video streaming over mesh networks.
A novel linear source model and a unified rate control algorithm for H.263/MPEG-2/MPEG-4

Let /spl rho/ be the percentage of zeros among the quantized transform coefficients. We discover that, in any typical video coding system, there is always a strictly linear relationship between /spl rho/ and the actual coding bit rate R. This linearity leads to a novel and unified source model for different types of source data and different coding systems, such as H.263, MPEG-2, and MPEG-4. The proposed linear source model is much simpler, but much more accurate than other source models reported in the literature. Based on this source model, a unified rate control algorithm is proposed for the above three video coding systems. Despite its extreme simplicity, the proposed algorithm outperforms other rate control algorithms by providing more accurate and robust rate control.
Object-level bit allocation and scalable rate control for MPEG-4 video coding

In this work, a novel framework for rate-distortion (R-D) analysis is developed. Based on this framework, an object-level bit allocation scheme is proposed for MPEG-4 video coding to determine the target number of bits for each video object. We then propose a macroblock-level rate control algorithm to achieve the bits target for each video object. The proposed bit allocation and rate control algorithm outperforms the VM8 rate control algorithm by providing more accurate rate regulation and significantly improved picture quality.
Novel coding scheme for wavelet image compression

In this paper, we propose a very simple image compression scheme. The proposed coding scheme employs the multi-level dyadic wavelet decomposition, linear quantization with a proper dead zone, and then it converts the quantized wavelet coefficients into three 1-D sequences for adaptive arithmetic coding. First, some of the clustered zeros in the multi-level dyadic wavelet decomposition are represented with the quadtree code, and then the remaining zeros and non-zero integers are arranged as a 1-D sequence by raster scanning. Next, the resulting integer sequence is decomposed into two 1-D sequences with small alphabet. Despite the simplicity of the proposed coding scheme, the rate-distortion performance of the proposed image compression algorithm is competitive with the best image coders in the literature.
/spl rho/-domain source modeling and rate control for video coding and transmission

The coding bit rate, R, is considered as a function of /spl rho/ which is the percentage of zeros among the quantized DCT (discrete cosine transform) coefficients. We discover that the rate function, R(/spl rho/), has some very interesting properties in the /spl rho/-domain. By introducing the new concepts of characteristic rate curves and rate curve decomposition, a novel framework for source modeling is proposed. Using the proposed source model, we can estimate the rate-quantization (R-Q) curve before quantization and coding with relative error less than 5%. Based on the estimated R-Q curve, the output bit rate of the video encoder can be accurately controlled. Our extensive simulation results show that the proposed algorithm outperforms TMN8 (see Ribas-Corbera, J. and Lei, S.,  1999) and VM7 (see Chiang, T. and Zhang, Y.-Q.,  1997) rate control algorithms by providing more accurate and robust rate control.
A unified rate-distortion analysis framework for transform coding: a summary

In our previous work, we have developed a rate-distortion (R-D) modeling framework H.263 video coding by introducing the new concepts of characteristic rate curves and rate curve decomposition. In this paper, we further show it is a unified R-D analysis framework for all typical image/video transform coding systems, such as EZW, SPIHT and JPEG image coding; MPEG-2, H.263, and MPEG-4 video coding. Based on this framework, a unified R-D estimation and control algorithm is proposed for all typical transform coding systems. We have also provided a theoretical justification for the unique properties of the characteristic rate curves. A linear rate regulation scheme is designed to further improve the estimation accuracy and robustness, as well as to reduce the computational complexity of the R-D estimation algorithm. Our extensive experimental results show that with the proposed algorithm, we can accurately estimate the R-D functions and robustly control the output bit rate or picture quality of the image/video encoder.
Coupled ensemble graph cuts and object verification for animal segmentation from highly cluttered videos

In this paper, we consider animal object segmentation from wildlife monitoring videos captured by motion-triggered cameras, called camera-traps. This is a very challenging task because the wildlife monitoring scenes are often highly cluttered and dynamic. To address this issue, we propose to explore the ideas of coupled ensemble graph cuts and object verification. We consider video object cut as an ensemble of frame-level background-foreground object classifiers which fuse information across frames and refine their segmentation results in a collaborative and iterative manner. To significantly reduce false positives in foreground animal detection and segmentation, we learn an object verification model to further classify if the segmented image patch belongs to the background or the animal. Our extensive experimental results and performance comparisons over a diverse set of challenging camera-trap data, as well as the new Change Detection 2014 benchmark dataset, demonstrate that the proposed framework outperforms various state-of-the-art algorithms and has the capability to handle even the most challenging objects in a wide variety of video sequences.
Integrated camera and sensor networks: Algorithms, design, and applications

Recent technological advances in hardware miniaturization of sensors, low-power microprocessor design, and wireless ad hoc networking have enabled the deployment of large-scale wireless sensor networks. A wireless sensor network is a system of geographically distributed sensor nodes that communicate with each other over a dynamic and self-organizing wireless network. In a wireless video sensor network (WVSN), each sensor, equipped with video capture and processing capabilities, is tasked to capture digital video information about the target event or situation, and deliver the video data to the remote control unit for further information analysis and decision making.
Optimal bit allocation for low bit rate video streaming applications

Current rate control schemes in video coding standards do not have efficient frame-level bit allocation because of the inherent constraints in real-time encoding. In this paper, we assume an offline video encoding environment and proposed a rate control scheme based on optimal bit allocation for low bit rate streaming applications. Specifically, we apply a /spl rho/-domain rate-distortion (R-D) model, originally applied at macroblock (MB) level, to frame-level. Based on this frame-level R-D model and a two-pass encoding method, we are able to allocate bits among video frames in an optimal way so that video sequences can be coded at low bit rate with an improved quality. Experimental results demonstrate the proposed scheme is able to achieve not only noticeable reduction in average distortion but also a more consistent and smoother visual quality.
Automated estimation of elder activity levels from anonymized video data

Significant declines in quality of life for elders in assisted living communities are typically triggered by health events. Given the necessary information, such events can often be predicted, and thus, be avoided or reduced in severity. Statistics on activities of daily living and activity level over an extended period of time provide important data for functional assessment and health prediction. However, persistent activity monitoring and continuous collection of this type of data is extremely labor-intensive, time-consuming, and costly. In this work, we propose a method for automated estimation of activity levels based on silhouettes segmented from video data, and subsequent extraction of higher order information from the silhouettes. By building a regression model from this higher order information, our system can automatically estimate elder activity levels.
Simple and efficient wavelet image compression

We propose a simple but efficient wavelet image compression algorithm. The proposed coding scheme employs the multi-level dyadic wavelet decomposition, linear quantization with a proper dead zone, 1-D addressing complexity by raster scanning within subbands, variable length block coding, small alphabet representation of 1-D integer sequences, and adaptive arithmetic entropy coding. Despite the simplicity of the proposed coding scheme, the rate-distortion performance of the proposed image compression algorithm is competitive with the best image coders in the literature.
Towards a social virtual reality learning environment in high fidelity

Virtual Learning Environments (VLEs) are spaces designed to educate students remotely via online platforms. Although traditional VLEs such as iSocial have shown promise in educating students, they offer limited immersion that diminishes learning effectiveness. This paper outlines a virtual reality learning environment (VRLE) over a high-speed network, which promotes educational effectiveness and efficiency via our creation of flexible content and infrastructure which meet established VLE standards with improved immersion. This paper further describes our implementation of multiple learning modules developed in High Fidelity, a “social VR” platform. Our experiment results show that the VR mode of content delivery better stimulates the generalization of lessons to the real world than non-VR lessons and provides improved immersion when compared to an equivalent desktop version.
From rate-distortion analysis to resource-distortion analysis

The ultimate goal in communication system design is to control and optimize the system performance under resource constraints. As the communication paradigm evolves from the conventional desktop computing, wired, and centralized communication to current mobile, wireless, distributed, and massive communication, video encoding and transmission operate under more and more resource constraints. Therefore, there is a need to extend the traditional R-D analysis to resource-distortion analysis by incorporating the new resource constraints into the R-D analysis framework. In distributed and massive wireless video sensor networks, the resource utilization behaviors of individual video sensors should be well-coordinated through network-level rate allocation and optimum routing so as to maximize the overall performance.
Robust Generalized Low-Rank Decomposition of Multimatrices for Image Recovery

Low-rank approximation has been successfully used for dimensionality reduction, image noise removal, and image restoration. In existing work, input images are often reshaped to a matrix of vectors before low-rank decomposition. It has been observed that this procedure will destroy the inherent two-dimensional correlation within images. To address this issue, the generalized low-rank approximation of matrices (GLRAM) method has been recently developed, which is able to perform low-rank decomposition of multiple matrices directly without the need for vector reshaping. In this paper, we propose a new robust generalized low-rank matrices decomposition method, which further extends the existing GLRAM method by incorporating rank minimization into the decomposition process. Specifically, our method aims to minimize the sum of nuclear norms and $l_{1}$ -norms. We develop a new optimization method, called alternating direction matrices tri-factorization method , to solve the minimization problem. We mathematically prove the convergence of the proposed algorithm. Our extensive experimental results demonstrate that our method significantly outperforms existing GLRAM methods.
Optimal retransmission timeout selection for delay-constrained multimedia communications

The RTO (retransmission timeout) selection is a fundamental problem for any acknowledgment-based transmission. Conventional approaches simply choose RTO according to the estimated round-trip time and do not consider the delay constraint of multimedia applications and the packet loss rates provided by communication networks. In this research, we propose a novel algorithm of optimal RTO selection for delay-constrained multimedia communications based on a network model. By appropriately choosing the RTO, we are able to achieve the optimal tradeoff between the error probability and the rate cost. Experimental results demonstrate the improved performance.
Distributed Rate Allocation and Performance Optimization for Video Communication Over Mesh Networks

Video streaming imposes high rate requirement and stringent constraints on resource limited mesh networks. In this work, we develop a distributed asynchronous particle swarm optimization (DAPSO) algorithm for resource allocation and performance optimization scheme for video communication over large-scale mesh networks. Unlike many network resource allocation performance optimization algorithms in the literatures which are able to handle convex network utility functions, the proposed scheme is able to handle generic nonlinear network utility functions. We will use a specific rate allocation and quality optimization problem for an example to demonstrate the efficiency of the proposed scheme and compare its performance with other algorithms, such as distributed gradient search.
Dynamic programming framework for automatic video object segmentation and vision-assisted video pre-processing

A generic definition of video objects, which is a group of pixels with temporal motion coherence, is considered. The generic video object (GVO) is the superset of the conventional video objects considered in the object segmentation literature. Because of its motion coherence, the GVO can be easily recognised by the human visual system. However, due to its arbitrary spatial distribution, the GVO cannot be easily detected by the existing algorithms which often assume the spatial homogeneousness of the video objects. The concept of extended optical flow is introduced and a dynamic programming framework for the GVO detection and segmentation is developed, whose solution is given by the Viterbi algorithm. Using this dynamic programming formulation, the proposed object detection algorithm is able to discover the motion path of the GVO automatically and refine its spatial region of support progressively. In addition to object segmentation, the proposed algorithm can also be applied to video pre-processing, removing the so-called ‘video mask’ noise in digital videos. Experimental results show that this type of vision-assisted video pre-processing significantly improves the compression efficiency.
Efficient edge-preserving stereo matching

A computationally efficient stereo matching algorithm is introduced providing high precision dense disparity maps via local aggregation approach. The proposed algorithm exploits a novel paradigm, namely separable successive weighted summation (SWS) among horizontal and vertical directions with constant operational complexity, providing effective connected 2D support regions based on local color similarities. The intensity adaptive aggregation enables crisp disparity maps which preserve object boundaries and depth discontinuities. The same procedure is also utilized to diffuse information through overlapped pixels during occlusion handling. According to the experimental results on Middlebury online stereo benchmark, the proposed method is one of the most effective local stereo algorithm providing high quality disparity models by unifying constant time filtering and weighted aggregation. Hence, the proposed algorithm provides a competitive alternative, with its efficient GPU and FPGA implementations, for various local methods in terms of achieving precise disparity maps from stereo video within fast execution time.
Watermarking for Image-Based Rendering via Homography-Based Virtual Camera Location Estimation

The recent advances in image based rendering (IBR) have pioneered freely determining the viewing position and angle in a scene from multi-view video. Remembering that a person could also record a personal video for this arbitrarily selected view and misuse this content, it is apparent that copyright and copy protection problems also exist and should be solved for IBR applications, as well. In our recent work (Alper Koz, 2006), we propose a watermarking method, which embeds the watermark pattern into every frame of multi-view video and extracts this watermark from a rendered image, generated by the nearest-interpolation based light-field rendering (LFR) and watermark detection is achieved for the cases in which the virtual camera could be arbitrarily located on the camera plane only. This paper presents an extension to the previous formulation for the rendered images, which are generated by using bilinear interpolation, namely the most attractive and promising interpolation method in LFR-based applications. Moreover, the location of the virtual camera could be completely arbitrary in this new formulation. The results show that the watermark could be extracted successfully for LFR via bilinear interpolation for any imagery camera location and rotation, as long as the visual quality of the rendered image is preserved.