nils-herrmann/clean_scirepeval huggingface dataset:
λ Domain based optimal bit allocation for scalable high efficiency video coding

Bit allocation is necessary for Scalable High Efficiency Video Coding (SHVC) rate control to provide optimal Rate-Distortion (R-D) performance under the bandwidth constraint. Since λ is the key factor to determine the bitrate and corresponding distortion, we seek to design a λ-domain optimal bit allocation algorithm for SHVC by taking the combined inter-layer and intra-layer dependency into consideration in this paper. We find out that the λ ratio between different pictures in the enhancement layer should be proportional to the influence of the current picture to the other enhancement layer pictures. While for the pictures in the base layer, the λ ratio should be proportional to the influence of the current picture to both the base layer and enhancement layer pictures. The experimental results demonstrate that the proposed bit allocation algorithm can bring obvious performance improvement.
Robust Blur Kernel Estimation for License Plate Images From Fast Moving Vehicles

As the unique identification of a vehicle, license plate is a key clue to uncover over-speed vehicles or the ones involved in hit-and-run accidents. However, the snapshot of over-speed vehicle captured by surveillance camera is frequently blurred due to fast motion, which is even unrecognizable by human. Those observed plate images are usually in low resolution and suffer severe loss of edge information, which cast great challenge to existing blind deblurring methods. For license plate image blurring caused by fast motion, the blur kernel can be viewed as linear uniform convolution and parametrically modeled with angle and length. In this paper, we propose a novel scheme based on sparse representation to identify the blur kernel. By analyzing the sparse representation coefficients of the recovered image, we determine the angle of the kernel based on the observation that the recovered image has the most sparse representation when the kernel angle corresponds to the genuine motion angle. Then, we estimate the length of the motion kernel with Radon transform in Fourier domain. Our scheme can well handle large motion blur even when the license plate is unrecognizable by human. We evaluate our approach on real-world images and compare with several popular state-of-the-art blind image deblurring algorithms. Experimental results demonstrate the superiority of our proposed approach in terms of effectiveness and robustness.
A Bayesian adaptive weighted total generalized variation model for image restoration

In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical parameter setting limits its performance. In this paper, we propose a novel Adaptive Weighted Total Generalized Variation model for image restoration. We analyze the TGV model from Bayesian Probability view and derive a novel adaptive parameter calculation scheme for it, exploiting the image's self-similarity. Experiment results on image deblurring and reconstruction show that by adapting the parameters in TGV model to image contents, the proposed model can restore image's edges and details well and achieve significant improvement over state of the art variational based models.
Denoising of Hyperspectral Images Employing Two-Phase Matrix Decomposition

Noise reduction is a significant preprocessing step for hyperspectral image (HSI) analysis. There are various noise sources, leading to the difficulty in developing a somewhat universal technique for noise reduction. A majority of the existing denoising strategies are designed to tackle a certain kind of noise, with somewhat idealized hypotheses imposed on them. Therefore, it is desirable to develop a noise reduction technique with high universality for various noise patterns. Matrix decomposition can decompose a given matrix into two components if they have low-rank and sparse properties. This fits the case of HSI denoising when an HSI is reorganized as a matrix, because the noise-free signal of HSI has low rank due to the high correlations within its content, while the noise of HSI has structured sparsity with respect to the big volume of data. Moreover, matrix decomposition avoids denoising process falling into the dependence on distribution characteristics of the noise or making some idealized assumptions on HSI signal and noise. In this paper, a two-phase matrix decomposition scheme is presented. First, by employing the low-rank property of HSI signal and the structured sparsity of HSI noise, the hyperspectral data matrix is decomposed into a basic signal component and a rough noise component. Then, the latter is further decomposed into a spatial compensation part and a final noise part, via using the band-by-band total variation (TV) regularization. A number of simulated and real data experiments demonstrate that the proposed approach produces superior denoising results for different HSI noise patterns within a wide range of noise levels.
Two-stage picture padding for high efficiency video coding

With the exponential growth of digital cameras and the aid of powerful video editing softwares, videos with various resolutions become ever more popular. Therefore, there is a great demand for video coding schemes supporting arbitrary resolutions with higher efficiency. In the latest standard, only a simple padding with direct copying is adopted to meet this requirement, and the coding efficiency is still unsatisfactory. To improve the performance, a novel two-stage picture padding is proposed in this paper. Based on the adopted transform, an efficient residual padding is employed in the first stage to minimize the burdened coding bits of padded parts. To obtain a reference picture with high-quality boundary, a template matching based approach is also proposed to rearrange the padded pixels in the second stage. Experimental results reveal that on top of HEVC, our method offers the performance with 2.1%, 1.8%, and 1.5% BD-rate reduction for RA-Main, LDB-Main and LDP-Main configurations, respectively. Compared with the state-of-the-art algorithm, it still outperforms for kinds of test sequences under all configurations.
Joint Source-Channel Secrecy Using Uncoded Schemes: Towards Secure Source Broadcast

This paper investigates a joint source-channel secrecy problem for the Shannon cipher broadcast system. We suppose list secrecy is applied, i.e., a wiretapper is allowed to produce a list of reconstruction sequences and the secrecy is measured by the minimum distortion over the entire list. For discrete communication cases, we propose a permutation-based uncoded scheme, which cascades a random permutation with a symbol-by-symbol mapping. Using this scheme, we derive an inner bound for the admissible region of secret key rate, list rate, wiretapper distortion, and distortions of legitimate users. For the converse part, we easily obtain an outer bound for the admissible region from an existing result. Comparing the outer bound with the inner bound shows that the proposed scheme is optimal under certain conditions. Besides, we extend the proposed scheme to the scalar and vector Gaussian communication scenarios, and characterize the corresponding performance as well. For these two cases, we also propose another uncoded scheme, orthogonal-transform-based scheme, which achieves the same performance as the permutation-based scheme. Interestingly, by introducing the random permutation or the random orthogonal transform into the traditional uncoded scheme, the proposed uncoded schemes, on one hand, provide a certain level of secrecy, and on the other hand, do not lose any performance in terms of the distortions for legitimate users.
Hybrid digital-analog scheme for video transmission over wireless

In this paper, we propose a novel wireless video transmission scheme named HDA-Cast, which is a hybrid digital-analog (HDA) coding scheme that integrates the advantages of digital coding and analog coding. Relative to most state-of-the-art video transmission methods, it avoids the “cliff effect” provided that the channel quality is within the expected range, gives better fairness among all receivers for multicast, and has strong adaption to channel variation. The evaluation results show that our HDA-Cast is 3.5-9.6 dB better than the SoftCast which is an up-to-date analog scheme. Owing to its strong adaption to channel variation, it can be regarded as a kind of wireless scalable video coding (WSVC).
Priority-based template matching intra prediction

Template matching intra prediction has been reported earlier, to generate intra prediction signal from wider reconstructed areas compared with H.264/AVC intra prediction. In this paper an improved intra prediction scheme which introduces priority-guided template matching is presented. The proposed method first calculates a priority value of each pixel on the border between the current to-be-predicted block and the reconstructed or predicted area, and then performs template matching for the template centered on the pixel with the highest priority among all the border pixels, in an iterative manner. Simulation results demonstrate that the proposed algorithm can save about 2% bitrate than the non-priority algorithm.
Sign language recognition with long short-term memory

Sign Language Recognition (SLR) aims at translating the Sign Language (SL) into speech or text, so as to facilitate the communication between hearing-impaired people and the normal people. This problem has broad social impact, however it is challenging due to the variation for different people and the complexity in sign words. Traditional methods for SLR generally use handcrafted feature and Hidden Markov Models (HMMs) modeling temporal information. But reliable handcrafted features are difficult to design and not able to adapt to the large variations of sign words. To approach this problem, considering that Long Short-Term memory (LSTM) can model the contextual information of temporal sequence well, we propose an end-to-end method for SLR based on LSTM. Our system takes the moving trajectories of 4 skeleton joints as inputs without any prior knowledge and is free of explicit feature design. To evaluate our proposed model, we built a large isolated Chinese sign language vocabulary with Kinect 2.0. Experimental results demonstrate the effectiveness of our approach compared with traditional HMM based methods.
Wireless Scalable Video Coding Using a Hybrid Digital-Analog Scheme

Wireless video broadcast/multicast and mobile video communication pose a challenge to the conventional video transmission scheme (which consists of separate digital source coding and digital channel coding). The reason is that the separate coding scheme is based on nonscalable coding design, and this unavoidably leads to cliff effect as well as limits the ability to support multiple users with diverse channel conditions. In this paper, we propose a novel wireless scalable video coding (WSVC) framework. Specifically, we present a hybrid digital-analog (HDA) joint source-channel coding (JSCC) scheme that integrates the advantages of digital coding and analog coding. Moreover, the proposed JSCC is able to broadcast one video with different resolutions to fit various devices with different display resolutions. Compared to most state-of-the-art video delivery methods, it avoids the staircase effect and realizes continuous quality scalability (CQS) on condition that the channel quality is within the expected range, and it has strong adaptability to channel variation with higher coding efficiency and better fairness among all receivers. Therefore, it is very suitable for wireless video broadcast/multicast transmission and mobile video applications. The experimental results show that for broadcasting/multicasting the videos with CIF and QCIF resolutions the proposed WSVC outperforms SoftCast (which is a new analog scheme) average 0.60-5.90 dB and 3.39-9.97 dB respectively, outperforms SVC+HM (which combines H.264/SVC codec and hierarchical modulation technique) average 3.87-9.13 dB and 0.27-10.47 dB respectively, and outperforms DCast (which is an up-to-date video delivery scheme) about 0.2-3.3 dB for the video with CIF resolution. The experimental results verify the effectiveness of our proposed WSVC framework.
Buffer requirement analysis and reference picture marking for temporal scalable video coding

Temporal scalable video coding is a useful feature supported in all existing video coding standards. Though coding efficiency is still the predominant performance metric, buffer requirement is another important factor for assessment of temporal scalable video coding methods. This paper presents analyses of minimum buffer requirement for temporal scalable coding based on the most typical temporal scalable video coding structure, i.e., the so-called hierarchical B picture coding structure. Minimum buffer sizes required for decoding of both the full bitstreams and thinned bitstreams are derived. After that, some reference picture management methods are proposed in order to enable both minimum buffer consumption and maximum coding efficiency. Comparative experiment results showing how different reference picture marking methods affect coding efficiency are also provided.
Hybrid transform for HEVC-based lossless coding

The High Efficiency Video Coding (HEVC) with the transform bypass mode is simple but inefficient for lossless coding. For this reason, we propose a novel transform to further eliminate the redundancy between residues of different blocks in intra prediction. Dependent on intra prediction modes, the proposed transform is adaptable to exploit correlations of residues formed by different modes. In order to accurately obtain parameters of the transform matrix, an approach similar to the Wiener filtering method is adopted. Experimental results show that on top of the lossless coding mode in HEVC, our method offers the performance with a 7.4% bit-rate reduction on average for All Intra Main configuration. Compared with other representative algorithms, our proposal still shows an improvement in the compression ratio, without substantial increases of computational complexity in the encoder or decoder.
Error Resilient Coding and Error Concealment in Scalable Video Coding

Scalable video coding (SVC), which is the scalable extension of the H.264/AVC standard, was developed by the Joint Video Team (JVT) of ISO/IEC MPEG (Moving Picture Experts Group) and ITU-T VCEG (Video Coding Experts Group). SVC is designed to provide adaptation capability for heterogeneous network structures and different receiving devices with the help of temporal, spatial, and quality scalabilities. It is challenging to achieve graceful quality degradation in an error-prone environment, since channel errors can drastically deteriorate the quality of the video. Error resilient coding and error concealment techniques have been introduced into SVC to reduce the quality degradation impact of transmission errors. Some of the techniques are inherited from or applicable also to H.264/AVC, while some of them take advantage of the SVC coding structure and coding tools. In this paper, the error resilient coding and error concealment tools in SVC are first reviewed. Then, several important tools such as loss-aware rate-distortion optimized macroblock mode decision algorithm and error concealment methods in SVC are discussed and experimental results are provided to show the benefits from them. The results demonstrate that PSNR gains can be achieved for the conventional inter prediction (IPPP) coding structure or the hierarchical bi-predictive (B) picture coding structure with large group of pictures size, for all the tested sequences and under various combinations of packet loss rates, compared with the basic joint scalable video model (JSVM) design applying no error resilient tools at the encoder and only picture copy error concealment method at the decoder.
Image deblocking via group sparsity optimization

Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity together, called group sparsity optimization. Instead of processing each image patch individually, in the proposed scheme, similar patches in one group are required to be well-represented on learned dictionary collaboratively, using group sparsity regularization. The group sparsity not only imposes every patch's representation to be sparse, bus also requires patches' coefficients in the group share the similar pattern. The experiment results on standard test images demonstrate that our scheme can improve the PSNR of the compressed images by an average of 1.25 dB, and outperform state of the art deblocking approaches.
Multiview-Video-Plus-Depth Coding Based on the Advanced Video Coding Standard

This paper presents a multiview-video-plus-depth coding scheme, which is compatible with the advanced video coding (H.264/AVC) standard and its multiview video coding (MVC) extension. This scheme introduces several encoding and in-loop coding tools for depth and texture video coding, such as depth-based texture motion vector prediction, depth-range-based weighted prediction, joint inter-view depth filtering, and gradual view refresh. The presented coding scheme is submitted to the 3D video coding (3DV) call for proposals (CfP) of the Moving Picture Experts Group standardization committee. When measured with commonly used objective metrics against the MVC anchor, the proposed scheme provides an average bitrate reduction of 26% and 35% for the 3DV CfP test scenarios with two and three views, respectively. The observed bitrate reduction is similar according to an analysis of the results obtained for the subjective tests on the 3DV CfP submissions.
Efficient and scalable cloud-assisted SVC video streaming through mesh networks

To improve the efficiency and scalability of video streaming in large scale mesh networks, we propose a cloud-assisted video delivering strategy that can distribute video contents to subscribers over multiple routing paths dynamically. By leveraging the computation power and numerous storage of a cloud and the flexibility of scalable video coding (SVC) scenarios, we design algorithms for multiple routing path selection and assignment, and propose to run them in a cloud for fast processing. The multi-path routing is then achieved by label-switching in the network layer. The simulation results show that the proposed algorithms achieved efficient usage of the network resources and improved user experience in terms of bandwidth, delay and jitter.
A new non-local video denoising scheme using low-rank representation and total variation regularization

In this article, we present a novel non-local video denoising scheme using low-rank representation and total variation regularization. The proposed scheme attempts to make full use of the intrinsic properties that the grouping similar patches not only lie in a low-rank subspace but are also sparse in total variation (TV) domain. For a group of similar patches, we formulate video denoising problem into a concise model that combines nuclear norm, TV regularization and l1 norm. The experiments demonstrate that the proposed scheme is capable of handling multi-type noise including dense Gaussian noise and random-valued sparse noise, while maintaining the texture information meantime. The results show that our scheme achieves noticeable performance improvement over the state-of-the-art video denoising methods.
Overview of the multiview high efficiency video coding (MV-HEVC) standard

This paper reviews the multiview extension (MV-HEVC) of the High Efficiency Video Coding (HEVC) standard. MV-HEVC is capable of multiview video coding with or without accompanying depth views. The key design concepts and design elements of MV-HEVC are described in the paper. Furthermore, the features and characteristics of MV-HEVC compared to other standardized video codec extensions for three-dimensional (3D) video coding are reviewed.
Parsing robustness in High Efficiency Video Coding - analysis and improvement

One problem in the current High Efficiency Video Coding design is that the entropy decoding, or the parsing process of one frame may depend on syntax elements on other frames. Thus, a small error may lead to immediate failure of decoding of the current frame and all the following inter frames. This paper analyzes the cause of this vulnerability of HEVC bitstreams. Based on the analysis, we further present two solutions to make a tradeoff between the coding efficiency and the error robustness by cutting down the error propagation caused by temporal motion vector prediction. One solution is to redesign some code tables so that the dependency on other frames' syntax elements can be broken. The other solution is to insert recovery points for the coded bitstream, so that the decoding process can recover from errors. The pros and cons of these two solutions are discussed. Experimental results show that the proposed methods can solve the parsing problem in HEVC with a marginal influence on the coding performance and provide significant improvements for the decoded video when there are errors.
Comments on “Approximate Characterizations for the Gaussian Source Broadcast Distortion Region”

Recently, Tian et al.  considered joint source-channel coding of transmitting a Gaussian source over K-user Gaussian broadcast channel, and derived an outer bound on the admissible distortion region. In , they stated “due to its nonlinear form, it appears difficult to determine whether it is always looser than the trivial outer bound in all distortion regimes with bandwidth compression”. However, in this correspondence we solve this problem and prove that for the bandwidth expansion case (with K ≥ 2), this outer bound is strictly tighter than the trivial outer bound with each user being optimal in the point-to-point setting; while for the bandwidth compression or bandwidth match case, this outer bound actually degenerates to the trivial outer bound. Therefore, our results imply that on one hand, the outer bound given in  is nontrivial only for Gaussian broadcast communication (K ≥ 2) with bandwidth expansion; on the other hand, unfortunately, no nontrivial outer bound exists so far for Gaussian broadcast communication (K ≥ 2) with bandwidth compression.
Multiview-video-plus-depth coding and inter-component prediction in high-level-syntax extension of H.265/HEVC

The multiview-video-plus-depth (MVD) format has become popular in representing 3D video content in a manner that enables free-viewpoint capability in the decoder side. The scalable (SHVC) and multiview (MV-HEVC) extensions of the High Efficiency Video Coding standard (H.265/HEVC) enable their functionality without additional coding tools and use the same high level syntax. In this paper, it is proposed to use the same coding approach as taken in SHVC and MV-HEVC for coding of MVD data. Furthermore, an inter-component motion vector prediction (ICP) method, which is realized through the temporal motion vector prediction (TMVP) mechanism of H.265/HEVC, is introduced to exploit the redundancy between texture and depth views. The experimental results show that 1.0% and 3.6% bitrate reduction can be achieved by ICP compared to independent coding of texture and depth for synthesized views and depth views, respectively, when the ICP method is applied to a 3-view coding scenario intended for multi-view autostereoscopic displays.