nils-herrmann/clean_scirepeval huggingface dataset:
Compressed sensing recovery of multi-view video sequences by targeted database

Compressed sensing (CS) is a technique that enables signal reconstruction at sub-Nyquist rate and has been widely used for fast reconstruction of multi-view video sequences (MVS) in the surveillance application. In this paper, we propose compressed-sensing recovery of MVS exploiting the model of structural group sparse representation (SGSR) along with a targeted database. SGSR groups similar patches together coupled with learning the adaptive basis from the similar groups, which gets sparser representation and thereby performs better in CS recovery. A targeted database can be easily obtained from the MVS due to their abundant prior information and the database can help us obtain more accurate similar patches, which further improve the performance with SGSR. Considering images as compressible signals rather than sparse signals, we design a filtering to retain the details of images. Simulation results show that the proposed algorithm outperforms existing reconstruction algorithms.
Distributed Lossless Coding Techniques for Hyperspectral Images

In this paper, we present a novel distributed coding scheme for lossless, progressive and low complexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly different from consumer images. Among them, lossless compression, progressive transmission, and low complexity onboard processing are three most prominent ones. To satisfy these requirements, we design a distributed coding scheme that shifts the complexity of data decorrelation to the decoder side to achieve lightweight onboard processing after image acquisition. At the encoder, the images are subsampled in order to facilitate successive encoding and progressive transmission. At the decoder, we generate the side information with adaptive region-based predictor by taking full advantage of the decoded subsampled images and previously decoded neighboring bands based on the assumptions that the objects appearing in different bands are highly correlated. The proposed progressive transmission via subsampling enables the spectral correlation to be refined successively, resulting in gradually improved decoding performance of higher-resolution layers as more sub-images are decoded. Experimental results on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data demonstrate that the proposed scheme is able to achieve competitive compression performance comparing with the-state-of-the-art 3D schemes, including existing distributed source coding (DSC) schemes. The proposed scheme has even lower encoding complexity than that of the conventional 2D schemes.
Multiuser Effective Capacity analysis for Queue Length Based Rate Maximum wireless scheduling

Recently the Effective Capacity of multi-user wireless scheduling has been analyzed based on large deviation principle. The users' queue length distribution bound of wireless scheduling algorithm such as round robin and rate maximum was discussed based on Effective Capacity analysis. But for Queue Length Based (QLB) Rate Maximum scheduling algorithm, the analysis result is only limited to system bound performance and not for each user's bound performance. In this paper we consider each user's amount of input traffic and channel statistical characteristics and introduce a new Effective Capacity analysis model for QLB scheduling algorithm. The queue length distribution bound of the QLB scheduling algorithm for each user is then predicted by the proposed analysis model. Based on the predicted queue length distribution bound, we can set different queue length threshold, that is, delay constraint (queue length can be translated to delay) for each user to obtain different queue length violation (or delay constraint violation or queue overflow) probability. Then the effect of each user's amount of input traffic to their queue length (or delay constraint) violation probability in multiuser environment is analyzed. The proposed analysis model and the estimation results have been verified by numerical simulations.
An Opportunistic Multi Rate MAC for reliable H.264/AVC video streaming over Wireless Mesh Networks

We present in this paper a scheme of reliable transmission of H.264/AVC video over Wireless Mesh Networks (WMNs) based on an Opportunistic Multi Rate (OMR) IEEE 802.11 MAC Strategy. This OMR strategy employs the channel reservation control packets at the MAC layer to exchange timely Channel Quality Estimation (CQE) information for an adaptive adjustment of spectrally efficient transmission rate at the physical layer. Such rate adaptation scheme offers an optimized transmission to guarantee the minimum packets delay and drop rate needed for time bound applications, such as video. Since IEEE 802.11 standard does not adopt a multi-rate MAC to support time critical applications, we need to resolve the problem associated with IEEE 802.11 standard by designing a novel adaptive rate architecture with minor modification on IEEE 802.11 MAC protocol based on CQE. This integrated scheme allows the physical layer to achieve the optimal transmission via smart FEC scheme implemented in the application layer. We evaluate the proposed scheme based on network-level metrics, including bit rate, packets delay and drop rate, in comparison with the state-of-the-art Opportunistic Auto-Rate (OAR) MAC scheme based on IEEE 802.11 WMNs. Extensive simulation results based on H.264/AVC video transmission for both proposed OMR scheme and OAR MAC have confirmed that the proposed OMR strategy outperforms the OAR MAC by a significant margin as CQE and smart FEC is able to adapt to the dynamic network conditions of IEEE 802.11 WMNs in a timely fashion.
No-Reference Depth Assessment Based on Edge Misalignment Errors for T + D Images

The quality of depth is crucial in all depth-based applications. Unfortunately, the error-free ground truth is often unattainable for depth. Therefore, no-reference quality assessment is very much desired. This paper presents a novel depth quality assessment scheme that is completely different from conventional approaches. In particular, this scheme focuses on depth edge misalignment errors in texture-plus-depth (T + D) images and develops a robust method to detect them. Based on the detected misalignments, a no-reference metric is calculated to evaluate the quality of depth maps. In the proposed scheme, misalignments are detected by matching texture and depth edges through three constraints: 1) spatial similarity; 2) edge orientation similarity; and 3) segment length similarity. Furthermore, the matching is performed on edge segments instead of individual pixels, which enables robust edge matching. Experimental results demonstrate that the proposed scheme can detect misalignment errors accurately. The proposed no-reference depth quality metric is highly consistent with the full-reference metric, and is also well-correlated with the quality of synthesized virtual views. Moreover, the proposed scheme can also use the detected edge misalignments to facilitate depth enhancement in various practical texture-plus-depth-based applications.
$\lambda $ -Domain Optimal Bit Allocation Algorithm for High Efficiency Video Coding

Rate control typically involves two steps: bit allocation and bitrate control. The bit allocation step can be implemented in various fashions depending on how many levels of allocation are desired and whether or not an optimal rate–distortion (R-D) performance is pursued. The bitrate control step has a simple aim in achieving the target bitrate as precisely as possible. In our recent research, we have developed a <inline-formula> <tex-math notation="LaTeX">$ {\lambda }$ </tex-math></inline-formula>-domain rate control algorithm that is capable of controlling the bitrate precisely for High Efficiency Video Coding (HEVC). The initial research showed that the bitrate control in the <inline-formula> <tex-math notation="LaTeX">$ {\lambda }$ </tex-math></inline-formula>-domain can be more precise than the conventional schemes. However, the simple bit allocation scheme adopted in this initial research is unable to achieve an optimal R-D performance reflecting the inherent R-D characteristics governed by the video content. In order to achieve an optimal R-D performance, the bit allocation algorithms need to be developed taking into account the video content of a given sequence. The key issue in deriving the video-content-guided optimal bit allocation algorithm is to build a suitable R-D model to characterize the R-D behavior of the video content. In this paper, to complement the R-<inline-formula> <tex-math notation="LaTeX">$ {\lambda }$ </tex-math></inline-formula> model developed in our initial work, a D-<inline-formula> <tex-math notation="LaTeX">$ {\lambda }$ </tex-math></inline-formula> model is properly constructed to complete a comprehensive framework of <inline-formula> <tex-math notation="LaTeX">$ {\lambda }$ </tex-math></inline-formula>-domain R-D analysis. Based on this comprehensive <inline-formula> <tex-math notation="LaTeX">$ {\lambda }$ </tex-math></inline-formula>-domain R-D analysis framework, a suite of optimal bit allocation algorithms are developed. In particular, we design both picture-level and basic-unit-level bit allocation algorithms based on the fundamental R-D optimization theory to take full advantage of the content-guided principles. The proposed algorithms are implemented in HEVC reference software, and the experimental results demonstrate that they can achieve an obvious R-D performance improvement with a smaller bitrate control error. The proposed bit allocation algorithms have already been adopted by the Joint Collaborative Team on Video Coding and integrated into the HEVC reference software.
Scene adaptive multiple coding scheme for robust image transmission

We propose a combined source and channel coding scheme for image transmission over noisy channels. The key component is extracting and preserving the scene information and incorporating it with unequal error protection to combat the channel errors. After hierarchical wavelet decomposition of the image, wavelet coefficients with a parent-child relationship are grouped into wavelet blocks and classified according to corresponding scenes in the original image. For each class, spatial neighborhood coefficients in the high frequency subbands are constrained so that the spatially isolated coefficients are removed and clustered coefficients are retained at the same time. All the wavelet blocks in the same class are grouped together and coded using the set partitioning in hierarchical trees (SPIHT) algorithm. High source coding efficiency is preserved even though multiple source coded bitstreams are generated since the wavelet tree structure is intact. In order to combat the channel errors, an unequal error protection strategy implemented by RCPC/CRC channel coding is designed based on the bit contribution to both PSNR and human visual sensitivity. Finally, a postprocessing method is developed at the receiving end to restore the degradation due to the residual error after channel decoding. Experimental results show that the proposed scheme is indeed able to provide protection for more important bits and more important visual content under a noisy transmission environment. In particular, the reconstructed images illustrate consistently better visual quality.
Toward Guaranteed Video Experience: Service-Aware Downlink Resource Allocation in Mobile Edge Networks

Video delivery has been playing an essential role in video services over edge networks. Although HTTP segment-based streaming, e.g., Dynamic Adaptive Streaming over HTTP (DASH), has become the prevailing technique, it cannot provide guaranteed video playback in terms of bitrate to mobile users. In essence, HTTP streaming downloads the video segments in a best effort fashion, i.e., passively responding to the channel dynamics. This can cause unstable playback with frequent rebuffer and multi-client competition that degrades a network-wide performance. In this paper, we present a network-assisted streaming framework for Guaranteed Playback-Experience Streaming over HTTP (GESH) that leverages the proactive control of network resources and joint coordination among multiple clients for service-aware network resource allocation. Specifically, GESH is empowered by a new weighted proportional fair scheduling without modifying existing cellular infrastructure, a per-segment channel variation model, and a suite of algorithms to seek the optimal weights for the scheduling. Extensive evaluations show that GESH can maximally guarantee the video playback of multiple users, as well as significantly outperforming conventional HTTP streaming and current DASH systems.
A novel two-pass VBR coding algorithm for fixed-size storage application

We propose a novel variable bit rate (VBR) algorithm that adopts two-pass encoding to implement high-performance coding for fixed-size storage applications. In the first-pass encoding, we use the original frame as the reference for motion estimation. This way, we are able to obtain the exact relationship between the amount of bits generated for each frame (rate) and each possible quantization factor (Q) so that an R-Q function can be built. The corresponding computational expense is virtually the same as a typical VBR encoder. With this R-Q function, we can optimize the quantization factor for each frame throughout the entire sequence based on the constraints in total storage size and decoder buffer size before the second-pass encoding is executed. This procedure not only guarantees full use of the fixed storage size for the compressed bitstream, but also prevents the decoder buffer from underflowing. Unlike a typical constant bit rate (CBR) decoder, this VBR decoder is capable of preventing the buffer from overflowing by introducing the pausing operation at some designated time. Because the quantization factor optimization is a simple table lookup, the computational requirement is minimized. In the second-pass encoding, the estimated quantization factors for all the frames through the above procedures is used to encode the entire video sequence as a free VBR encoder. Experimental results on real video sequences show that the proposed VBR encoding can provide more consistent visual quality and an improved coding efficiency. The application of the proposed VBR coding scheme includes video streaming over the Internet, digital versatile disk, digital library, and video on demand.
Multi-objective content preserving warping for image stitching

Image stitching has been developed to enhance users' immersive experiences by constructing new images of higher spatial resolution and broader field of view from multiple input images. Most image stitching schemes assume 2D transformation of the whole frame between images. There usually exist local misalignments of contents that do not match with the transformation and may cause artifacts such as ghosting. Several schemes including seam cutting, image blending and other image composition algorithms, have been developed to relieve such artifacts. However, these schemes are essentially post-stitching processing that do not address the alignment problem and would fail when the parallax is large. In this paper, we propose a multi-objective content preserving warping (CPW) for local adjustment of image alignment which is optimized considering feature matching, line preserving, line matching, shape smoothness and other factors to significantly reduce the visual artifacts. In particular, we adjust the image warping from their pre-warping results using the homography estimated during the image registration step. Experimental results show that the proposed CPW scheme for image stitching is able to generate much smoother stitching results comparing with the state-of-the-art methods reported in the literature.
A novel product coding and recurrent alternate decoding scheme for image transmission over noisy channels

In this letter, we present a novel product channel coding and decoding scheme for image transmission over noisy channels. Two convolutional codes with at least one recursive systematic convolutional code are employed to construct the product code. Received data are decoded alternately in two directions. A constrained Viterbi algorithm is proposed to exploit the detection results of cyclic redundancy check codes so that both reduction in error patterns and fast decoding speed are achieved. Experiments with image data coded by the algorithm of set partitioning in hierarchical trees exhibit results better than those currently reported in the literature.
Joint transceiver beamforming design and power allocation for multiuser MIMO systems

Multi-user multiple input multiple output (MU-MIMO) system is an excellent choice for the next generation broadband communications because of its great potential in enhancing MIMO system capacity. One major challenge of such system is the jointly optimal transceiver beamforming design that maximizes the sum capacity under a total power constraint. Existing approaches have provided convex optimization solution by exploiting the duality between transmitter and receiver. However, with high computation complexity, these approaches are ineligible of practical implementation. In this paper, we aim to provide complexity efficient transceiver beamformer and power allocation design in MU-MIMO downlink (broadcast) channels. We first develop an iterative sub-optimal algorithm based on cyclic self-SINR-maximization (CSSM) beamforming design and water-filling power allocation. Comparing to convex-optimization-based (COB) approaches, the proposed solution (i.e. CSSM-WF algorithm) have insignificant sum-rate degradation but very low computational complexity and extremely fast converging speed. Trying to improve the performance of CSSM-WF algorithm, we then introduce another algorithm denoted as efficient COB (ECOB) algorithm in which CSSM-WF algorithm is used to generate a good start point for optimal COB approaches. Simulation results prove the efficiency of both proposed algorithms.
A novel volumetric feature extraction technique, with applications to MR images

The in vivo morphological and volumetric analysis of biological structures is a topic which is of increasing interest in the medical community. Such an analysis requires the extraction of the feature of interest from volumetric magnetic resonance (MR), ultrasound (US) or computed tomography (CT) scans. The difficulty of this extraction varies with the modality used and the morphology of the structure of interest, but in many cases current practice requires the manual outlining of the structure on each slice of the scanned volume-a time consuming and tedious task. We seek to provide an automated algorithm which accomplishes this task with minimal a priori knowledge and operator input and with an accuracy which is comparable to that of the current manual and semi-automated techniques.
On a new adaptive quantization scheme in subband-based video coding

This paper presents a new adaptive quantization scheme for subband video coding using wavelets. The authors aim at designing a quantization that is both signal adaptive and scene adaptive. By scene adaptive, the authors mean that the quantization is able to shift its bit allocation to perceptually more important coefficients so that the preservation of scene structure can be achieved. This new quantization scheme is based on the principle of adaptive clustering with spatial neighborhood constraints characterized by the Gibbs random field. With such spatial constraints, it is able to reach beyond the limit of the traditional simple statistical models that all lack of scene adaptivity. In addition, the dynamically enforced spatial constraints are able to overcome the artificial block division which is often the major source of distortion when coding at low bit rate. The authors have successfully applied this scheme to videoconferencing and obtained very favorable results. The authors believe that this new adaptive quantization will have an impact on many other applications because it is able to perform simultaneously signal adaptive and scene adaptive quantization.
Multi-Level Video Frame Interpolation: Exploiting the Interaction Among Different Levels

This paper proposes a novel multi-level frame interpolation scheme by exploiting the interactions among different levels. The proposed scheme includes three major stages that work at block level, pixel level, and sequence level, respectively. Effective algorithms are designed for each stage, i.e., block-level motion estimation with dropping unreliable motion vectors, pixel-level motion vector-guided partial scale-invariant feature transform flow matching, and sequence-level 3-D total variation regularized completion. Compared to traditional methods that focus mostly at one single level, the proposed scheme manages to recognize and utilize the interactions among the three levels based on their distinct characteristics and intertwined relationships. With a proper exploitation of interactions, unique advantages for each level can be effectively preserved while inherent limitations of a given level can be overcome by utilizing information from other levels. Extensive experiments have confirmed its superior performance over several classical schemes, in both subjective visual quality and objective peak signal-to-noise ratio/structure similarity measurements, and typical artifacts can be significantly reduced.
Technical challenges in video coding and processing for future digital entertainment

This talk will present some technical challenges in video coding and processing to meet the paradigm shifting trends for future digital entertainment for consumers. Traditional consumer video services have been in the broadcasting mode, from terrestrial TV, to satellite and cable services, in which a single encoder is able to serve millions of decoders. The design principle has been the simple decoder of volume sets at the expense of very complicated encoder. The proliferation of mobile devices with video capture capabilities in the recent years has resulted in a paradigm shift trends that require simple encoder for the mobile devices. The burden of the performance has now shifted to decoder that resides at consumerpsilas home to manage volumetric video captures with desktop computers. This paradigm shift thus created an opportunity for new generations of video coding and processing algorithms and architectures to meet the challenges in more complicated video decoding. In this talk, we will present several examples of new video coding and processing schemes based on distributed source coding. Some detailed analysis and simulation results will be shown to demonstrate that distributed source coding based approach is indeed promising for video decoding and processing for future digital entertainment.