nils-herrmann/clean_scirepeval huggingface dataset:
Subband Coupling Aware Rate Allocation for Spatial Scalability in 3-D Wavelet Video Coding

The motion compensated temporal filtering (MCTF) technique, which is extensively used in 3-D wavelet video coding schemes nowadays, leads to signal coupling among various spatial subbands because motion alignment is introduced in the temporal filtering. Using all spatial subbands as a reference enables MCTF to fully take advantage of temporal correlation across frames but inevitably brings drifting problem in supporting spatial scalability. This paper first analyzes the signal coupling phenomenon and then proposes a quantitative model to describe signal propagation across spatial subbands during the MCTF process. The signal propagation is modeled for a single MC step based on the shifting effect of wavelet synthesis filters and then it is extended to multilevel MCTF. This model is called subband coupling aware signal propagation (SCASP) model in this paper. Based on the model, we further propose a subband coupling aware rate allocation scheme as one possible solution to the above dilemma in supporting spatial scalability. To find the optimal rate allocation among all subbands for a specified reconstruction resolution, the SCASP model is used to approximate the reconstruction process and derive the synthesis gain of each subband with regard to that reconstruction. Experimental results have fully demonstrated the advantages of our proposed rate allocation scheme in improving both objective and subjective qualities of reconstructed low-resolution video, especially at middle bit rates and high bit rates.
A practical algorithm for tanner graph based image interpolation

This paper interprets image interpolation as a decoding problem on tanner graph and proposes a practical belief propagation algorithm based on a gaussian autoregressive image model. This algorithm regards belief propagation as a way to generate and fuse predictions from various check nodes. A low complexity implementation of this algorithm measures and distributes the departure of current interpolation result from the image model. Convergence speed of the proposed algorithm is discussed. Experimental results show that good interpolation results can be obtained by a very small number of iterations.
GPU based fast algorithm for tanner graph based image interpolation

In image/video processing software and hardware products, low complexity interpolation algorithms, such as cubic and splines methods, are commonly used. However, these methods tend to blur textures and produce jaggy effect compared with other adaptive methods such as NEDI, SAI. Tanner graph based image interpolation algorithm has better effect in dealing with edge and texture, but with high computation complexity. Thanks to the high performance parallel processing capability of today's GPU, use of complex algorithms for real time application is becoming possible. In this paper, we present a fast algorithm for tanner graph based image interpolation and it's implementation on GPU. In our algorithm, the image model training process of tanner graph based image interpolation is greatly simplified. Experimental results show that the GPU implementation can be more than 47 times as fast as the CPU implementation.
Adaptive loop filter with temporal prediction

In this paper, we propose a method to improve adaptive loop filter (ALF) efficiency with temporal prediction. For one frame, two sets of adaptive loop filter parameters are adaptively selected by rate distortion optimization. The first set of ALF parameters is estimated by minimizing the mean square error between the original frame and the current reconstructed frame. The second set of filter parameters is the one that is used in the latest prior frame. The proposed algorithm is implemented in HM3.0 software. Compared with the HM3.0 anchor, the proposed method achieves 0.4%, 0.3% and 0.3% BD bitrate reduction in average for high efficiency low delay B, high efficiency low delay P and high efficiency random access configuration, respectively. The encoding and decoding time increase by 1% and 2% on average, respectively.
Side information extrapolation with temporal and spatial consistency

In this paper, we present an efficient side information extrapolation scheme with temporal and spatial consistency for low-delay Wyner-Ziv video coding. Our method is based on the regularized local linear regression (RLLR) model, in which each pixel in SI is approximated as a linear weighted combination of samples within a local temporal neighborhood. The optimal model parameters are estimated by projecting the transformation function onto the temporal training samples to exploit motion-related dependency. During this procedure, moving weights are incorporated into the objective function to express the relative importance of training samples in estimating parameters of the model. Furthermore, spatial correlation is explored by imposing an additional local smoothness penalty, which does good to estimate the occluded regions and complex motion regions. The learned function is smooth and locally linear, and can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results demonstrate that the RLLR method achieves very competitive SI extrapolation performance compared with the state-of-the-art methods.
Superimposed Modulation for Soft Video Delivery With Hidden Resources

Analog-transmission-based soft video delivery suffers from the leveling-off effect when the allocated channel bandwidth is severely insufficient. Fortunately, with superimposed modulation, it is possible for analog traffic to share bandwidth with digital traffic. In this paper, we design and analyze such a hybrid digital-analog superimposed modulation (HDA-SIM) scheme for soft video delivery. Unlike previous work, we treat the bandwidth of competing digital traffic as hidden resources for the video delivery system. The key problem in this scheme is how to allocate the bandwidth and power resources among various modulation symbols so that we can improve the performance of video delivery without sacrificing the throughput of existing digital traffic. The resource allocation problem is formulated and the optimal solution under any given channel signal-to-noise ratio is derived. Based on the results, the sufficient and necessary condition for the video delivery system to achieve performance gain is given. In addition, we implement the proposed scheme for two state-of-the-art soft video delivery systems known as SoftCast and SharpCast. Both simulations and testbed evaluations show that the HDA-SIM version can achieve significant gains in the received video quality over their original designs.
Content-adaptive low rank regularization for image denoising

Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of the input image to adaptively model the prior distribution. The proposed scheme is based on the observation that, for a natural image, a matrix consisted of its vectorized non-local similar patches is of low rank. We use a non-convex smooth surrogate for the low-rank regularization, and view the optimization problem from the empirical Bayesian perspective. In such framework, a parameter-free distribution prior is derived from the grouped non-local similar image contents. Experimental results show that the proposed approach is highly competitive with several state-of-art denoising methods in PSNR and visual quality.
Performance analysis of transform in uncoded wireless visual communication

In wireless scenarios where the channel condition may vary drastically, visual communication systems using source and channel coding generally suffer from threshold effect. An uncoded transmission scheme called SoftCast [1]-[3], however, was recently shown to provide both graceful quality transition and competitive performance. In SoftCast, image signal is directly modulated to a dense constellation using proper power for transmission, solely after employing a transform for energy compaction, leaving out conventional quantization, entropy coding and channel coding. The received signal is lossy in nature, with its noise level commensurate with the channel condition. This paper presents a theoretical analysis for uncoded visual communication, focusing on the role of transform and the quantitative measurement of transform gain in a generalized uncoded transmission framework with optimal power allocation. Our analysis reveal that the energy distribution among signal elements plays an important role in the power-distortion performance. Further analysis show that the energy compaction capability of decorrelation transform can bring significant gain by boosting the energy diversity in signal representation. Numerical analysis results are reported for Markov random signals and natural images, respectively. The performance of typical transforms, e.g. KLT, DCT and DWT, and the effect of different transform sizes or levels are evaluated. These analysis results are verified by simulations.
CG-Cast: Scalable Wireless Image SoftCast Using Compressive Gradient

G-Cast is a wireless visual communication scheme that conveys visual information via image gradient. It is inspired by the characteristics of human vision systems and can provide improved perceptual quality. G-Cast is power efficient but bandwidth demanding, because gradient data have double the size of the original image. This paper presents a scheme named CG-Cast for scalable image transmission in bandwidth-limited wireless scenarios. It employs a compressive-gradient-based image representation to describe perceptually sensitive image details and reduce the bandwidth requirement at the same time, combining gradient-based visual representation with compressive sensing techniques. The compressive gradient data are transmitted in a pseudo-analog way so that it achieves elegant quality transition in a wide channel signal-to-noise ratio (CSNR) range. CG-Cast also sends a small set of low-frequency data in digital a way to provide the global and local luminance of the image. We developed an effective optimization algorithm for the decoder to reconstruct the original image from the received noisy compressive gradient and the low-frequency part of the image. Experimental results demonstrate that the proposed scheme improves the quality of received images remarkably under different CSNR and channel bandwidth conditions.
Correlation estimation for distributed wireless video communication

One important problem in distributed video coding is to estimate the variance of the correlation noise between the video signal and its decoder side information. This variance is hard to estimate due to the lack of the motion vectors at the encoder side. In this paper, we first propose a linear model to estimate this variance by referring the zero motion prediction at the encoder based on a Markov field assumption. Furthermore, not only the prediction noise from the video signal itself but also the additional noise due to wireless transmission is considered in this paper. We applied our correlation estimation method in our recent distributed wireless visual communication framework called DCAST. The experimental results show that the proposed method improves the video PSNR by 0.5-1.5dB while avoiding motion estimation at encoder.
Image compressive sensing using overlapped block projection and reconstruction

Compressive sensing allows a signal to be sampled at sub-Nyquist rate and still get recovered exactly, if the signal is sparse in some domain. Block compressive sensing (BCS) is advocated for practical image compressive sensing, since it processes image at block level and significantly reduces the memory requirement for storing projection matrix. However, existing BCS methods process blocks separately, which breaks the continuity between blocks and usually produces blocking artifacts. This paper proposes a new image compressive sensing scheme using overlapped-block projection and reconstruction (OBPR), in which the sampling is performed on overlapped blocks. During reconstruction, the sparsity constraint in transform domain is also enforced on the overlapped blocks. An augmented Lagrangian method is used to solve the optimization problem efficiently. Experimental results show that the proposed OBPR scheme achieves significantly better results than the existing BCS schemes in reconstruction quality.
Improved autoregressive image model estimation for directional image interpolation

For image interpolation algorithms employing autoregressive models, a mechanism is required to estimate the model parameters piecewisely and accurately so that local structures of image can be exploited efficiently. This paper proposes a new strategy for better estimating the model. Different from conventional schemes which build the model solely upon the co-variance matrix of low-resolution image, the proposed strategy utilizes the covariance matrix of high-resolution image itself, with missing pixels properly initialized. To make the estimation robust, we adopt a general solution which exploits the covariance matrices of both scales. Experimental results demonstrate that the proposed strategy improves model estimation and the interpolation performance remarkably.
Layered motion estimation and coding for fully scalable 3D wavelet video coding

This paper proposes a framework of scalable motion estimation and coding with the structure of multilayers for 3D wavelet video coding. The motion representation consists of multiple layers. The encoder uses motion of all layers to perform analysis, while the decoder may receive only part of motion for synthesis. Different from other schemes, each layer of motion is a point optimized at a certain range of bit-rate. We observe that the distortion introduced by motion mismatch is highly independent with the rate for texture in a wide range. Therefore, to make the best trade-off between motion and texture under the constraint of a given bit rate, a motion layer decision algorithm is used to find the appropriate number of motion layers to be included into the bit-stream. The proposed framework also supports the spatial and temporal scalabilities of motion. Experimental results show significant improvement at low bit-rates and nearly no loss at high bit-rates with layered motion coding and optimal motion decision. The performance is approaching to the convex hull of those with multiple sets of nonscalable motion.
Compound image compression by multi-stage prediction

Computer generated compound images contain not only photographic images but also text and graphics images, which makes it difficult to be efficiently compressed using exiting image and video coding standards. This paper presents a new compound image coding scheme based on the intra coding framework of the upcoming high efficiency video coding (HEVC) standard. The proposed scheme first decomposes the compound image into color components and structure components. Then two-stage prediction scheme is employed to exploit the correlation among structure components, where the first prediction is generated by directional prediction and second prediction is generated by template matching. Experimental results show that the proposed scheme achieves up to 10.2 dB coding gain on compound images compared with HEVC. And on average it achieves 42.2 percent bitrate saving compared with HEVC.
Hybrid Digital–Analog Video Delivery With Shannon–Kotel’nikov Mapping

Hybrid digital–analog (HDA) transmission is beco-ming an attractive solution for mobile video delivery because it not only has graceful degradation with channel variations but also yields high power efficiency. However, the heavy bandwidth demand of analog transmission is still an unsolved problem, limiting the received video quality when the bandwidth is not sufficient. To address this problem, we propose adopting Shannon–Kotel’nikov (SK) mapping for HDA video transmission and design an HDA scheme called SK-Cast. SK-Cast consists of a digital and an analog branch. In the digital branch, SK-Cast compresses the video sequence using an high efficiency video coding digital encoder to produce a base layer. The base layer is transmitted through digital methods with strong protection. The residual signals are then decorrelated using three-dimensional discrete cosine transform transform. The SK mapping is exploited to transmit these coefficients, as they can achieve efficient bandwidth compression. We address the resource allocation problems in SK-Cast, including the allocation between digital and analog branches and the allocation among analog symbols. The simulation results show that the SK-Cast outperforms the state-of-the-art HDA systems, including WSVC and SharpCast, and a digital scalable video coding system.
WaveCast: Wavelet based wireless video broadcast using lossy transmission

Wireless video broadcasting is a popular application of mobile network. However, the traditional approaches have limited supports to the accommodation of users with diverse channel conditions. The newly emerged Softcast approach provides smooth multicast performance but is not very efficient in inter frame compression. In this work, we propose a new video multicast approach: WaveCast. Different from softcast, WaveCast utilizes motion compensated temporal filter (MCTF) to exploit inter frame redundancy, and utilizes conventional framework to transmit motion information such that the MVs can be reconstructed losslessly. Meanwhile, WaveCast transmits the transform coefficients in lossy mode and performs gracefully in multicast. In experiments, Wave-Cast outperforms softcast 2dB in video PSNR at low channel SNR, and outperforms H.264 based framework up to 8dB in broadcast.
Macroblock-Based Adaptive In-Scale Prediction for Scalable Video Coding

This paper extends the so called generalized in-scale coding technique developed in our previous work to a macroblock-based adaptive in-scale motion compensation framework for H.264/MPEG-4 SVC (scalable video coding). In this framework, the in-scale technique is employed to generate predictions for video frames at high resolution layers, which can utilize both temporal correlation and cross-resolution correlation simultaneously. The lowpass content of a high resolution frame is predicted from its lower resolution layer and the highpass content is predicted from neighboring frames at the same resolution layer. For quality and resolution combined scalability, the prediction from lower resolution layer is not always better than temporal prediction. The in-scale technique is thereby employed adaptively at macroblock level according to the local characteristics of video signal. Techniques for motion estimation and mode decision are also investigated. Experimental results demonstrate that the scheme proposed in this paper can outperform H.264/MPEG-4 SVC significantly. The coding performance is quite promising especially for high-fidelity video coding
Gradient based image transmission and reconstruction using non-local gradient sparsity regularization

Most existing image coding and communication systems aim to minimize the mean square error (MSE) of the pixels reconstructed at receivers. However, the quality metric MSE has long been criticized for not being consistent with the perception of human vision systems. This paper considers a gradient-based image SoftCast (G-Cast) scheme, based on the recent advancements in image quality assessment which indicate that gradient similarity is highly correlated with perceptual image quality. To reconstruct the image from the received noisy gradient data, we exploit the statistical characteristics of image gradients. Instead of using the very simple Laplacian distribution for image gradient as in the total variation (TV) model, we further exploit the non-local similarity of image patches. A non-local gradient sparsity regularization (NLGSR) method is developed and solved using augmented Lagrangian method. Experimental results show that the proposed scheme provides promising perceptual image quality, and the NLGSR reconstruction scheme outperforms the existing schemes remarkably.
Image Interpolation Via Regularized Local Linear Regression

The linear regression model is a very attractive tool to design effective image interpolation schemes. Some regression-based image interpolation algorithms have been proposed in the literature, in which the objective functions are optimized by ordinary least squares (OLS). However, it is shown that interpolation with OLS may have some undesirable properties from a robustness point of view: even small amounts of outliers can dramatically affect the estimates. To address these issues, in this paper we propose a novel image interpolation algorithm based on regularized local linear regression (RLLR). Starting with the linear regression model where we replace the OLS error norm with the moving least squares (MLS) error norm leads to a robust estimator of local image structure. To keep the solution stable and avoid overfitting, we incorporate the ℓ2-norm as the estimator complexity penalty. Moreover, motivated by recent progress on manifold-based semi-supervised learning, we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points. Specifically, our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art interpolation algorithms, especially in image edge structure preservation.
Optimal LR-PET protection for scalable video streams over lossy channels with random delay

This paper investigates the optimal PET protection for streaming scalably compressed streams over networks where the delivery time constraints allow limited retransmissions (LR) and the communication channels exhibit both random losses and delays. A key property must be considered in this scenario is the possibility that a packet successfully arrives at the receiver in time, even if its acknowledgment is not received by the sender at certain deadlines. This paper proposes an extended LRPET scheme, namely random-delay LR-PET, in which additional streams may be sent to provide supplemental protection for the packets whose acknowledgments are still missing at a specified time after the transmission. To determine the optimal protection in each transmission opportunity, hypotheses concerning the number of acknowledged packets and the effect of future retransmission are considered. As the key contribution of this paper, we develop a method to derive the effective overall recovery probability versus redundancy characteristic, which significantly simplifies the actual protection assignment procedure. This paper also demonstrates the benefits of the optimization strategy proposed for this random-delay LR-PET scheme and the cruciality of time selection for scheduling retransmission.