nils-herrmann/clean_scirepeval huggingface dataset:
Side information enhancement using an adaptive hash-based genetic algorithm in a Wyner-Ziv context

Side information construction in Wyner-Ziv video coding is a sensible task which strongly influences the final ratedistortion performance of the scheme. This side information is usually generated through an interpolation of the previous and next images. Some of the zones of a scene however, such as the occlusions, cannot be estimated with other frames. In this paper we propose to avoid this problem by sending some hash information for these unpredictable zones of the image. The resulting algorithm is described and tested here. The obtained results show the advantages of using localized hash information for the high error zones in distributed video coding.
Highly scalable video coding by bidirectional predict-update 3-band schemes

Motion-compensated temporal filtering is an essential ingredient of recently developed wavelet-based scalable video coding schemes. The lifting implementation of these decompositions represents a versatile tool for spatio-temporal optimizations and numerous improvements have thus been proposed. An additional feature has been introduced lately by providing a 3-band lifting temporal decomposition, allowing non-dyadic scalability factors. The paper presents a motion-compensated scheme which is the 3-band equivalent of the 5/3 temporal filterbank. The proposed structure does not enter the classical lifting framework and therefore its invertibility is studied. Simulation results show higher coding efficiency, improved visual quality and more flexibility compared to the previously introduced Haar-like 2 or 3-band schemes.
Lms Based Adaptive Prediction for Scalable Video Coding

3D video codecs have attracted recently a lot of attention, due to their compression performance comparable with that of state-of-art hybrid codecs and due to their scalability features. In this work, we propose a least mean square (LMS) based adaptive prediction for the temporal prediction step in lifting implementation. This approach improves the overall quality of the coded video, by reducing both the blocking and ghosting artefacts. Experimental results show that the video quality as well as PSNR values are greatly improved with the proposed adaptive method, especially for video sequences with large contrast between the moving objects and the background and for sequences with illumination variations
A Cross-Layer Approach with Adaptive Rate Allocation and Quantization for Return Channel Suppression in Wyner-Ziv Video Coding Systems

In this paper, we present a novel technique for the suppression of the return channel in a multisensor distributed Wyner-Ziv video coding system. The proposed technique relies on a cross-layer approach that takes into account the transmission channel conditions on one hand, and the content of the captured video scenes on the other, in order to allocate different transmission rates and dynamically vary the quantization parameter for each user. Simulation results show significant improvement in the average system performance compared to a traditional system where all users are assigned equal channel resources and a fixed number of quantization levels.
Evaluation of multicasting schemes based on joint multiple description and network coding

This paper considers a multicast scenario and compares the average reception quality obtained when combining multiple description coding (MDC) and network coding (NC). Plain (single description) network coding (NC-SDC) serves as reference. In the considered scenario, a single source is multicast to several receivers with various channel conditions. Contrary to a NC-SDC scheme, unable to recover the coded packets when not enough combinations of packets have been received, NC of MDC packets allows a more progressive quality improvement with the number of received packets, and a reduction of the effect of the quantization noise when MDC is performed via frame expansion before quantization. Considering a probability distribution for the bit transition probability during transmission to any user in the multicast group, the expected signal-to-noise ratio is evaluated. Performance comparisons are made for various error distributions, field sizes, and MDC methods (via frame expansion and correlating transform).
A simple Multiple Description Coding scheme for improved peer-to-peer video distribution over mobile links

This paper presents an adaptation of peer-to-peer diffusion strategies of video over mobile links based on the use of multiple description coding. The system architecture, including the used overlay algorithm (the unstructured GIA algorithm) and video coding with multiple description coding (MDC), are presented as well as their interest in this context. Simulation results illustrate the approach, showing the interest of using MDC vs. a single description coding solution. The proposed simulation framework involves an H.264/AVC video codec with two descriptions and a simple adaptation of the overlay algorithm.
Joint source-channel coding with partially coded index assignment for robust scalable video

A scalable video coder consisting of motion-compensated temporal filtering coupled with structured vector quantization plus a linear mapping of quantizer indexes that minimizes simultaneously source and channel distortions is presented. The linear index assignment takes the form of either a direct, uncoded mapping or a coded mapping via Reed-Muller codes. Experimental results compare the proposed system to a similar scheme using unstructured vector quantization as well as to a prominent scalable video coder protected by more traditional convolutional codes. The proposed system consistently outperforms the other two schemes by a significant margin for very noisy channel conditions.
A variational framework for simultaneous motion and disparity estimation in a sequence of stereo images

In this paper, we present a variational framework for joint disparity and motion estimation in a sequence of stereo images. The problem involves the estimation of four dense fields: two motion fields and two disparity fields. In order to reduce computational complexity and improve estimation accuracy, the two motion fields, for the left and right sequences, and the disparity field of the current stereo pair are jointly estimated, using the stereo-motion consistency constraint. In the proposed variational framework, the joint estimation problem is formulated as a convex programming problem in which a convex objective function is minimized under specific convex constraints. This minimization is achieved using an efficient parallel block-iterative algorithm. Experimental results involving real stereo sequences indicate the feasibility and robustness of our approach.
A convex programming bit allocation method for sparse sources

The objective of this paper is to design an efficient bit allocation algorithm in the subband coding context based on an analytical approach. More precisely, we consider the uniform scalar quantization of subband coefficients modeled by a Generalized Gaussian distribution. This model appears to be particularly well-adapted for data having a sparse representation in the wavelet domain. Our main contribution is to reformulate the bit allocation problem as a convex programming one. For this purpose, we firstly define new convex approximations of the entropy and distortion functions. Then, we derive explicit expressions of the optimal quantization parameters. Finally, we illustrate the application of the proposed method to wavelet-based coding systems.
Complexity scalable motion compensated wavelet video encoding

We present a framework for the systematic analysis of video encoding complexity, measured in terms of the number of motion estimation (ME) computations, that we illustrate on motion compensated wavelet video coding schemes. We demonstrate the graceful complexity scalability of these schemes through the modification of the spatiotemporal decomposition structure and the ME parameters, and the use of spatiotemporal prediction. We generate a wide range of rate-distortion-complexity (R-D-C) operating points for different sequences, by modifying these options. Using our analytical framework we derive closed form expressions for the number of ME computations for these different coding modes and show that they accurately capture the computational complexity independent of the underlying content characteristics. Our framework for complexity analysis can be combined with rate-distortion modeling to determine the encoding structure and parameters for optimal R-D-C tradeoffs.
A convex-optimization framework for frame-level optimal rate allocation in predictive video coding

Optimal rate allocation is among the most challenging tasks to perform in the context of predictive video coding, because of the dependencies between frames induced by motion compensation. In this paper, we derive an analytical rate-distortion model that explicitly takes into account the dependencies between frames. The proposed approach allows us to formulate the frame-level optimal rate allocation as a convex optimization problem. Within this framework, we are able to achieve the exact solution in limited time (even for large-size problems), thanks to the flexibility offered by recent convex optimization techniques. Experiments on standard sequences demonstrate the interest of considering the proposed rate-distortion model and confirm that the optimal rate allocation ensures a better distribution of the total bit budget, with superior results (in the rate-distortion sense) with respect to the standard H.264/AVC rate control.
Map estimation of the input of an oversampled filter bank from noisy subbands by belief propagation

Oversampled filter banks perform a subband decomposition with redundancy representation. This redundancy has been shown to be useful to combat channel impairments, when the subbands are transmitted over a wireless channel, as well as quantization noise. This paper describes an implementation of the maximum a posteriori and the minimum mean-square error (MMSE) estimators of the input signal from the noisy quantized subbands obtained at the output of some transmission channel. The relations between the input samples and the noisy subband samples are described using a factor graph. Belief propagation is then applied to get the posterior marginals of the input samples. The experimental results show that when the channel is clear, a linear MMSE estimate performs quite well but the proposed approaches perform significantly better than a reconstruction using the linear MMSE estimator when the channel is noisy: a gain in terms of channel SNR of more than 2 dB is observed.
Adaptive update lifting with gradient criteria modeling high-order differences

This paper treats a class of adaptive update lifting schemes which do not require bookkeeping for perfect reconstruction. The choice of the update lifting filter is triggered by a binary threshold criterion based on a generalised gradient which is chosen in such a way that it only smoothes homogeneous regions. More precisely, the criterion can be chosen so that it ‘recognises’ signals that are polynomial up to a given order. The lifting filter updates the signal in these polynomial regions but leaves other parts unaffected.
Rotated Constellations for Video Transmission Over Rayleigh Fading Channels

A joint source-channel coding scheme for transmission of video over flat Rayleigh fading channels is described. The coding scheme consists of a spatiotemporal motion-compensated wavelet decomposition, a vector quantization of the coefficients through maximum-diversity lattices, and a linear labeling which minimizes simultaneously the source and channel distortion. Modulation diversity via rotated constellations produces the maximum-diversity lattices which increase robustness to channel fading without the addition of redundancy. Experimental results compare the proposed system to a prominent scalable video coder protected by more traditional convolutional codes, and superior performance is observed for high levels of channel noise.
Improved side information generation for distributed video coding

In distributed video coding, the side information is commonly generated by motion-compensated temporal interpolation of the neighboring reference frames at the decoder side. The side information quality has a strong impact on the final rate-distortion performance of the codec. In this paper, we propose a successive refinement after the decoding of each DCT subband to improve the accuracy of motion compensation between reference frames, in order to obtain a new side information estimation closer to the original Wyner-Ziv frame. Here, we propose two different algorithms in the refinement process of the side information. The experimental results show that the proposed techniques allow an improvement in rate-distortion performance that can reach 1.15 dB for GOP size of 2 and 3.2 dB for longer GOP size, with respect to state-of-the-art techniques, for sequences containing high motion.
Fusion of Global and Local Motion Estimation for Distributed Video Coding

The quality of side information plays a key role in distributed video coding. In this paper, we propose a new approach that consists of combining global and local motion compensation at the decoder side. The parameters of the global motion are estimated at the encoder using scale invariant feature transform features. Those estimated parameters are sent to the decoder in order to generate a globally motion compensated side information. Conversely, a locally motion compensated side information is generated at the decoder based on motion-compensated temporal interpolation of neighboring reference frames. Moreover, an improved fusion of global and local side information during the decoding process is achieved using the partially decoded Wyner-Ziv frame and decoded reference frames. The proposed technique improves significantly the quality of the side information, especially for sequences containing high global motion. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.9 dB for a Group of Pictures (GOP) size of 2, and up to 4.65 dB for larger GOP sizes, with respect to the reference DISCOVER codec.
Image denoising by adaptive lifting schemes

In this paper, we study the problem of image denoising by using an adaptive lifting scheme. Such a scheme can adapt itself well to the analyzed signal, which allows to keep important information for denoising applications. However, it results in a non-isometric transform which can be an important limitation as most of the denoising approaches rely on the estimation of the noise energy in the subbands. In a previous work, it has been shown how to evaluate the subband energies of an uncorrelated signal, in the wavelet domain when using such an adaptive scheme. Based on this previous work, we propose in this paper an estimation of the noise energies in the subband and use it to perform image denoising. Experimental results illustrate that this approach is more effective, in image denoising, than the classical non adaptive lifting schemes both considering perceptual and non perceptual image quality measures.
RD Optimized Coding for Motion Vector Predictor Selection

The H.264/MPEG4-AVC video coding standard has achieved a higher coding efficiency compared to its predecessors. The significant bitrate reduction is mainly obtained by efficient motion compensation tools, as variable block sizes, multiple reference frames, 1/4-pel motion accuracy and powerful prediction modes (e.g., SKIP and DIRECT). These tools have contributed to an increased proportion of the motion information in the total bit- stream. To achieve the performance required by the future ITU-T challenge, namely to provide a codec with 50% bitrate reduction compared to the current H.264, the reduction of this motion information cost is essential. This paper proposes a competing framework for better motion vector coding and SKIP mode. The predictors for the SKIP mode and the motion vector predictors are optimally selected by a rate-distortion criterion. These methods take advantage from the use of the spatial and the temporal redundancies in the motion vector fields, where the simple spatial median usually fails. An adaptation of the temporal predictors according to the temporal distances between motion vector fields is also described for multiple reference frames and B-slices options. These two combined schemes lead to a systematic bitrate saving on Baseline and High profile, compared to an H.264/MPEG4-AVC standard codec, which reaches up to 45%.
Depth-aided image inpainting for novel view synthesis

Depth Image Based Rendering (DIBR) technique has been recognized as a promising tool for supporting advanced 3D video services required in MultiView Video (MVV) systems. However, an inherent problem with DIBR is to fill newly exposed areas (holes) caused by disocclusions. This paper addresses the disocclusion problem. To deal with small disocclusions, hole-filling strategies have been designed by the state-of-the-art through pre-processing techniques of the depth video. For larger disocclusions, where depth pre-processing has some limitations, we propose an inpainting approach to retrieve missing pixels. Specifically, we propose in the texture and structure propagation process to take into account the depth information by distinguishing foreground and background parts of the scene. Experimental results illustrate the efficiency of the proposed method.
A genetic algorithm for side information enhancement in distributed video coding

This work aims at improving the quality of the side information in distributed video coding. Based on genetic algorithms, our proposed technique combines several frames, interpolated using previously developed methods, in a fusion-based approach. Simulation results show a significant improvement in the side information quality compared to other interpolation techniques available in the literature, which greatly improves the rate-distortion performance of a distributed video codec, where the gain in PSNR can reach 6 dB.
High order motion interpolation for side information improvement in DVC

A key step in distributed video coding is the generation of the side information (SI) i.e. the estimation of the Wyner-Ziv frame (WZF). This step is also frequently called image interpolation. State-of-the-art techniques perform a motion estimation between adjacent key frames (KFs) and linear interpolation in order to assess object positions in the WZF, and then the SI is produced by motion compensating the KFs. However the uniform motion model underlying this approach is not always able to produce a satisfying estimation of the motion, which can result in a low SI quality. In this paper we propose a new method for the generation of SI, based on higher order motion interpolation. We use more than two KFs to estimate the position of the current WZF block, which allows us to correctly estimate more complex motion (such as, for example, uniform accelerated motion). We performed a number of tests for the fine tuning of the parameters of the method. Our experiments show that the new interpolation technique has a small computational cost increase with respect to state of the art, but provides remarkably better performance with up to 0.5 dB of PSNR improvement in SI quality. Moreover the proposed method performs consistently well for several GOP sizes.
Low-Latency Video Streaming With Congestion Control in Mobile Ad-Hoc Networks

In this paper, we address the challenge of delivering a video stream, encoded with multiple descriptions, in a mobile ad-hoc environment with low-latency constraints. This kind of application is meant to provide an efficient and reliable video communication tool in scenarios where the deployment of an infrastructure is not feasible, such as military and disaster relief applications. First, we present a recently proposed protocol that employs a reliable form of one-hop broadcast to build an efficient overlay network according to a multi-objective function that minimizes the number of packets injected in the network and maximizes the path diversity among descriptions. Then, we introduce the main contribution of this paper: a cross-layer congestion control strategy where the MAC layer is video-coding aware and adjusts its transmission parameters (namely, the RTS retry limit) via congestion/distortion optimization. The main challenge in this approach is providing a reliable estimation of congestion and distortion, given the limited information available at each node. Our simulations show that, if a stringent constraint of low delay is imposed, our technique grants a consistent gain in terms of both PSNR and delay reduction, for bitrates up to a few megabits per second.
Dense disparity estimation in multiview video coding

Multiview video coding is an emerging application where, in addition to classical temporal prediction, an efficient disparity prediction should be performed in order to achieve the best compression performance. A popular coder is the multiview video coding (MVC) extension of H.264/AVC, which uses a block-based disparity estimation (just like temporal prediction in H.264/AVC). In this paper, we propose to improve the MVC extension by using a dense estimation method that generates a smooth disparity map with ideally infinite precision. The obtained disparity is then segmented and efficiently encoded by using a rate-distortion optimization technique. Experimental results show that significant gains can be obtained compared to the block-based disparity estimation technique used in the MVC extension.
Quantization of adaptive 2D wavelet decompositions

Classical linear wavelet representations of images have the drawback that they are not well suited to represent edge information. To overcome this problem, nonlinear multiresolution decompositions are being designed that can take into account the characteristics of the input signal/image. In our previous work [(G. Piella et al., July 2002), (H.J.A.M. Heijmans et al., 2002)] we have introduced an adaptive lifting framework that does not require bookkeeping but has the property that it processes edges and homogeneous regions in an image in a different fashion. The current paper discusses the effects of quantization in such adaptive wavelet decomposition. We provide conditions for recovering the original decisions at the synthesis and for relating the reconstruction error to the quantization error. Such an analysis is essential for the application of these adaptive decompositions in image compression algorithms.
Spatio-temporal grouping with constraint for seam carving in video summary application

Efficient tools to perform video summarization become essential as the number of cameras for video surveillance is growing exponentially. The development of automatic algorithms to aid human operators in identifying what is important and keeping it in a video summary becomes essential. This paper proposes a new way to obtain video summary based on seam carving. An efficient spatio-temporal grouping is done to determine the temporal rate of reduction depending on the content, to suppress groups of isolated seams, to identify spatio-temporal groups of seams and to approximate by constant segments the number of seams for each group, while keeping the total sum of seams constant. Problems of geometric distortion, anachronism and length of summary have been successfully addressed. The approach has been tested on video sequences of road traffic and leads to good visual results.