nils-herrmann/clean_scirepeval huggingface dataset:
Compressive video sampling from a union of data-driven subspaces

Recently, compressive sampling (CS) is an active research field of signal processing. To further decrease the necessary measurements and get more efficient recovery of a signal x, recent approaches assume that x lives in a union of subspaces (UoS). Unlike previous approaches, this paper proposes a novel method to sample and recover an unknown signal from a union of data-driven subspaces (UoDS). Instead of a fix set of supports, this UoDS is learned from classified signal series which are uniquely formed by block matching. The basis of these data-driven subspaces is regularized after dimensionality reduction by principal component extraction. A corresponding recovery solution with provable performance guarantees is also given, which takes full advantage of block-sparsity structure and improves the recovery efficiency. In practice, the proposed scheme is fulfilled to sample and recover frames in video sequences. The experimental results demonstrate that the proposed video sampling behaves better performance in sampling and recovery than the classical CS.
Iterative Thresholding-Based Sparse Directional Representation for Efficient Low Bit-Rate Embedded Video Coding

The contourlet transform provides a flexible directional image decomposition by employing Laplacian pyramid and uniform directional filter banks. Although it is able to efficiently capture the 2-D piecewise smooth functions with line or curve discontinuities, its major drawbacks of 4/3 redundancy and non-ideal filter banks in Laplacian pyramid are the main obstacles for high performance image/video coding. In this paper, we propose a video coding scheme based on the sharp frequency localized contourlet transform (contourlet/SFL) under the sparse representation framework, in which the iterative thresholding algorithm is applied to get an l1-norm sparser version of the transform coefficients. In the meantime, considering the fact that strong inter-band and inter-scale dependencies exist in the contourlet/SFL coefficients, a directional embedded image coding system is proposed to propagate the significance status by using the neighbor, cousin and parent significant coefficients as seeds. Experimental results show that the coding performance and visual quality of the sparse contourlet/SFL based video compression scheme are superior to the wavelet-based one, especially on those sequences full of directional structures at low bit rates.
Depth propagation with tensor voting for 2D-to-3D video conversion

Recently, 2D-to-3D video conversion has raised great interest because of the increasing demand of 3D video contents. A critical component in semi-automatic methods is to estimate the depth information of non-key frames from key frames. To overcome the sensitiveness to occlusion in existing schemes, a depth propagation method is proposed in this paper where tensor voting is leveraged to eliminate occlusion points, thereby derive accurate sparse depth. Furthermore, high dimensional tensor is constructed with coordinate, motion and color features to represent points in the sparse depth. A dense depth can be generated via tensor-voting based interpolation. It is robust against object occlusion and inaccurate motion estimation because tensor voting can efficiently make denoising and capture the structures. Experiment results demonstrate that the proposed method outperforms state-of-the-art semi-automatic techniques.
Delay-power-rate-distortion model for H.264 video coding

In a video encoding system, the encoding performance would be determined by not only the rate-distortion (R-D) behavior, but also the encoding time and power consumption. To analyze and control the R-D behavior of the video encoding system under the encoding delay and energy constraints, in this paper, we extend the traditional R-D model to a novel delay-power-rate-distortion (d-P-R-D) model by including another two dimensions (the encoding time and power consumption), which quantifies the relationship among source encoding delay, rate, distortion and power consumption for IPPPP coding mode in H.264/AVC. Here, I and P stand for intra-coding and predictive coding, respectively. The model accuracy has been verified through experiments and compensated by considering the statistics of both the current frame and the previous frame.
A 3D Self-Adjust Region Growing Method for Axon Extraction

Neuron axon analysis is an important means to investigate disease mechanisms and signaling pathways in neurobiology and often requires collecting a great amount of statistical information and phenomena. Automated extraction of axons in 3D microscopic images posts a key problem in the field of neuron axon analysis. To address tortuous axons in 3D volumes, a self-adjust region growing approach referring to surface modeling and self-adjustment which takes advantage of the nature of axon (e.g., continuity), is presented. Experimental results on axon volumes show that the proposed scheme provides a reliable solution to axon retrieving and overcomes several common drawbacks from other existing methods.
Priority Belief Propagation-Based Inpainting Prediction With Tensor Voting Projected Structure in Video Compression

This paper presents a new video compression framework over H.264/AVC scheme that integrates our proposed structured priority belief propagation (BP)-based inpainting prediction (IP) to exploit the intrinsic nonlocal and geometric regularity in video samples. Unlike the existing edge-based inpainting adopted in lossy image coding, the optimal predictor could maintain the pixel-wise fidelity and the robust error resilience without any assistant information. Beyond the local prediction limitation of traditional intra and inter-modes, the priority BP with regularized structure priors of a spatio-temporal Markov random field is imposed on the predictor in an adaptive and more convergent sense. Specifically, the structured sparsity of the predicted macroblock region is inferred by tensor voting projected from the co-located decoded regions. In turn, the priority and visiting order of nodes are assigned according to the sets of updated beliefs as the propagation of messages. Through relatively few iterations of forward and backward process, the sparse inference of priority BP would ensure a stable marginal belief distribution on the structure and texture through updating local messages and beliefs. Within the optimal mode selection on rate-distortion optimization (RDO), the IP-mode with structured priority BP outperforms the existing vision-based approaches, and specially achieves a better objective rate-distortion performance besides visual quality. The IP-mode with structured priority BP can be applied to both I and P frames to generate low entropy residue, e.g., homogeneous visual patterns, and the computation complexity is also competitive with one iteration of sparse inference. Moreover, it behaves more resilient with an intrinsic probabilistic inference than the intra and inter-modes.
Cross-Layer Frame Discarding for Cellular Video Coding

In the case of delivering real-time video over the 3G cellular networks, burst frame losses may be inevitable and unpredictable, which may cause severe quality degradation. Based on cross-layer frame discarding (CLFD), this paper proposes an enhanced error-resilient video coding scheme for cellular video communication. By using unequal retransmission at the radio link (RL) layer, a base station can provide reliable transmission for the relatively important frames in one video sequence. Relying on the unequal protection at the RL layer, the encoder at the application (APP) layer can actively discard a certain number of frames according to the received acknowledgement messages. Thus, unpredictable burst frame losses during transmission can be transformed into selective frame discarding at the encoder. Experiments results show that the proposed scheme can enhance the error resilience of the cellular video communication significantly.
Kernelized learning in deep scattering convolution networks

This paper addresses the problem of automatic scattering feature selection for signal classification. While features derived from group invariant scattering networks are quite effective for signal classification. We argue that scattering networks are not always the appropriate choice as they are not learned for the objective at hand. In this paper, we explore jointly learning a deep scattering convolution network with a support vector machine by casting the problem as a multiple kernel learning problem. The convolution paths of the network are kernelized respectively to be selected in a large-margin context. We deduce scattering paths from the corresponding kernels after solving the kernel learning problem. Experiments on several datasets demonstrate the effectiveness of the proposed method over state-of-the-art techniques.
Revertible deep convolutional networks with iterated directional filter bank

Efficient image representation methods are of great significance in image processing. This paper proposes an invertible deep convolutional network where the entire architecture is constructed by a decomposition process and a frequency recombination process. The decomposition allows frequency division in both the lower and higher part at the same time on each layer. It is implemented by using iterated directional filter banks. Perfect reconstruction is available if we adopt biorthogonal qincunx filter banks. The remaining frequency recombination process is adjoined to the architecture to transform the uniform frequency partition to nonuniform one and thereby increase the efficiency and flexibility. Numerical experiments reveal that the underlying network has good performance especially for images with large amount of fine-grained information.
Adaptive predict based on fading compensation for lifting-based motion compensated temporal filtering

A lifting implementation of the discrete wavelet transform applied along motion trajectories has recently gained a lot of attention in the video community as strong candidates in incoming scalable video coders. We generalize the coding scheme for classical lifting-based motion compensation temporal filtering and permit the codec to choose adaptively between the original reference frames and new fading-compensated reference frames to predict residuals while maintaining the invertibility of the inter-frame transform. Experimental results show that the proposed algorithm not only significantly improves subjective visual quality of the temporal low-pass frames, but also has 0.15-0.3 dB gain in PSNR performance compared with the normal (5, 3) lifting schemes.
Design and implementation of an end-to-end system for DTV multicasting to home over broadband IP access network

This paper describes design and implementation of an end-to-end system which implements digital television (DTV) multicasting to the home over broadband IP access network. We have developed a DTV-IP gateway for introducing DTV programs from broadcasting network into IP network with special considerations of the challenges, which consist of different service models and different protocols when deploying DTV services between the traditional satellite, cable or terrestrial network and the broadband IP network. According to different application scenario, the DTV-IP gateway can output two types of streams: CBR (constant bit-rate) transport stream (TS) over IP and VBR (variable bit-rate) TS over IP. Moreover, we have proposed a method to auto-regenerate the standard CBR TS from VBR TS over IP for the standard set-top-box (STB) with consideration of timing synchronization. This end-to-end system has been verified to work well in the broadband IP access network environment.
Sparse Spatio-Temporal Representation With Adaptive Regularized Dictionary Learning for Low Bit-Rate Video Coding

For promising vision-based video coding on low-quality data, this paper proposes a sparse spatio-temporal representation with adaptive regularized dictionary learning and develops a low bit-rate video coding scheme. In a reversed-complexity Wyner–Ziv coding manner, it selects a subset of key frames to code at original resolution, while the rest are down sampled and reconstructed by a sparse spatio-temporal approximation using key frames as a training dataset. Since primitive patches (geometry) are of low dimensionality and can be well learned from the primitive patches across frames in a scale space, a video frame is divided into three layers: a primitive layer, a nonprimitive coarse layer, and a nonprimitive smooth layer. The multiscale differential feature representations are invertible to facilitate reconstruction with dictionary learning, and the target is formulated as an optimization problem by constructing a sparse representation of 2-D patches and 3-D volumes over adaptive regularized dictionaries, a set of 2-D subdictionary pairs trained from primitive patches, and a 3-D dictionary trained from nonprimitive volumes. Specifically, the nonprimitive layer is constructed as volumes in to order keep it consistent along the motion trajectory, which enables sparse representations over a learned 3-D spatio-temporal dictionary. Through hierarchical bidirectional motion estimation and adaptive overlapped block motion compensation, the 3-D low-frequency and high-frequency dictionary pair is designed by the K-SVD algorithm to update the atoms for optimal sparse representation and convergence. In reconstruction, the lost high-frequency information of the down-sampled frames can be synthesized from the sparse spatio-temporal representation over the adaptive regularized dictionaries. Extensive experiments validate the compression efficiency of the proposed scheme versus H.264/AVC in terms of both objective and subjective comparisons.
Bitwise Structured Prediction Model for Lossless Image Coding

In this paper, we propose the bitwise structured prediction model for the loss less image coding. The prediction problem is handled by decomposing into a series of bitwise prediction problems, where max margin estimation is made for each bit. Furthermore, the structured prediction is proposed for combining such bitwise problems with constraints on their interrelationship and loss function for loss-augmented inference. The proposed methods tend to utilize the inherent dependencies existing in the bitplanes among the neighboring pixels and suppress the fluctuation led by the bitwise decomposition. When building the max margin Markov network for training, the upper bound for the prediction errors is shown to be asymptotically equivalent to the results obtained over the training set. Experimental results also show that the proposed method is superior in coding performance for regular oscillatory patterns.
Design and implementation of HDTV encoder system with parallel processing architecture

This paper presents a design procedure for an HDTV encoder with parallel processing architecture, which could get out the dilemma lying in high-speed digital processing circuitry and real-time compression. In the proposed system, an original HDTV picture is split to multiple sub-pictures of MPEG-2 MP@ML level, and then multiple sub-picture encoding modules (SEM) perform, respectively and simultaneously, MPEG-2 coding in the light of a joint rate control scheme. A normative HDTV PES stream of MPEG-2 MP@HL is built up by compositing multiple ES streams with different bit-rate. The paper hits the high points and supply appropriate implementation strategy during the propose design framework.
A Source-Driven Error Recovery Scheme using Wyner-Ziv Coding

In this paper, we propose an error recovery scheme to cope with the frame loss problem in large end-to-end delay scenario. Because Wyner-Ziv coding can produce deterministic output with nondeterministic inputs, we adopt the decoder's error-corrupted reference picture as side information to exploit its correlation with source picture, and thus improve the coding efficiency. To prevent retransmission request, we propose a feasible encoder-driven rate estimation scheme by only storing MVs and part of the error pattern at encoder. Furthermore, a new Laplacian parameter computing method is proposed based on discrete Laplacian PDF. The experimental results show that the proposed estimation schemes have quite high precision, and the error recovery scheme outperforms INTRA refresh scheme up to 5 dB.
Global Correlated Data Gathering in Wireless Sensor Networks with Compressive Sensing and Randomized Gossiping

In this paper, we address the problem of access to global information from any single point in wireless sensor networks. To this end, we propose a distributed in-network data acquisition approach, on the basis of compressive sensing, in which sparse random projections and randomized gossiping are jointly designed. In the context of unreliable distributed wireless settings, a simple random gossip algorithm is adopted. It allows the sensor to forward the linear combined data (readings) to randomly chosen neighbors. Also, each transmission path of the combined data is mapped to one row of the projection matrix, forming low-cost sparse random projections. Moreover, a theoretical model is developed that exactly characterizes the relationship of the number of sparse projections, the degree of the sparsity, and the error probability. Finally, simulation results show that, compared with the conventional approach, the proposed algorithm can achieve better reconstruction quality with less communication overhead.
Joint Inference of Objects and Scenes With Efficient Learning of Text-Object-Scene Relations

The rapid growth of web images presents new challenges as well as opportunities to the task of image understanding. Conventional approaches rely heavily on fine-grained annotations, such as bounding boxes and semantic segmentations, which are not available for web-scale images. In general, images over the Internet are accompanied with descriptive texts, which are relevant to their contents. To bridge the gap between textual and visual analysis for image understanding, this paper presents an algorithm to learn the relations between scenes, objects, and texts with the help of image-level annotations. In particular, the relation between the texts and objects is modeled as the matching probability between the nouns and the object classes, which can be solved via a constrained bipartite matching problem. On the other hand, the relations between the scenes and objects/texts are modeled as the conditional distributions of their co-occurrence. Built upon the learned cross-domain relations, an integrated model brings together scenes, objects, and texts for joint image understanding, including scene classification, object classification and localization, and the prediction of object cardinalities. The proposed cross-domain learning algorithm and the integrated model elevate the performance of image understanding for web images in the context of textual descriptions. Experimental results show that the proposed algorithm significantly outperforms conventional methods in various computer vision tasks.
Spatial non-stationary correlation noise modeling for Wyner-Ziv error resilience video coding

Most of the Wyner-Ziv (WZ) video coding schemes in literature model the correlation noise (CN) between original frame and side information (SI) by a given distribution whose parameters are estimated in an offline process. In this paper, an online CN modeling algorithm is proposed towards a more practical WZ-based error resilient video coding (WZ-ERVC). In ERVC scenario, the side-information is typically generated from the error concealed picture instead of bi-directional motion prediction. The proposed online CN modeling algorithm achieves the so-called classification gain by exploiting the spatially non-stationary characteristics of the motion field and texture. The CN between the source and error concealed SI is modeled by a Laplacian mixture model, where each mixture component represents the statistical distribution of prediction residuals and the mixing coefficients portray the motion vectors estimation error. Experimental results demonstrate significant performance gains both in rate and distortion versus the conventional Laplacian model.
3D tubular structure extraction using kernel-based superellipsoid model with Gaussian process regression

To analyze the tubular structure correctly and obtain a record of the centerlines has become significantly more challenging and infers countless applications in a large amount of fields. Hence, a robust and automated technique for extracting the centerlines of the tubular structure is required. To address complicated 3D tubular objects, a novel kernel-based modeling approach with regard to minimizing tracking energy is presented in this paper. The 3D tubular structure can be demonstrated as a kernel-based superellipsoid model with non-uniform weights. To improve the performance, Gaussian process is also introduced to update the parameters of the kernel-based model, especially for the complicated structure with cross sections, varying radii, and complicated branches. At last, the extensive experimental results on 3D tubular data demonstrate that our proposed method deals effectively with complicated tubular structure.
Reconstruction for Distributed Video Coding: A Context-Adaptive Markov Random Field Approach

Within the existing reconstruction process of distributed video coding (DVC), there are two major approaches: the maximum probability reconstruction and the minimum mean square error (MMSE) reconstruction. Both of them assume that each node, a pixel in pixel domain DVC or a coefficient in transform domain DVC, is i.i.d., and reconstruct the value of each node independently by only exploiting statistical correlation between source and side-information. These kinds of models produce considerable amount of artifacts in decoded Wyner-Ziv (WZ) frames and degrade the objective performance. In this paper, we propose a context-adaptive Markov random field (MRF) reconstruction algorithm which exploits both the statistical correlation and the spatio-temporal consistency by modeling the corresponding MRF of a generic DVC architecture, and solve the inference by finding its MRF-based maximum a posteriori (MAP) estimate. The energy function of the MRF model consists of two terms: a data term measuring the statistical correlation, and a geometric regularity term enforcing local spatio-temporal structure consistency which is modeled by optical flow estimation with regard to the critical parameters under a wide variety of DVC scenarios. In case the unreliability of the derived local structure, a confidence parameter is introduced to prevent inappropriate penalizing. To find the reconstructed patch assignment with the largest expected probability in the context-adaptive MRF, the energy minimization for the MRF-based MAP estimate of the WZ frames is solved by global optimization and greedy strategies. Compared to the existing maximum probability and MMSE reconstruction with i.i.d. model, a better subjective and objective performance is validated by extensive experiments.
Privacy-Preserving Biometric System for Secure Fingerprint Authentication

Privacy is an important concern when biometrics are used in authentication systems for accessing Electronic Health Records (EHR) or other biomedical research data repositories involving human subjects. Biometrics of individuals deserve careful protection because they contain sensitive information closely related to personal privacy (e.g., personal health, ethnic group, etc.) and the leakage of such information can be used to re-identify individuals. More importantly, biometrics are unique and they are not easily revocable. Existing secure biometric systems prevent attackers from collecting unprotected biometrics in databases, however, they cannot guarantee confidentiality in probing and transmitting biometrics.