nils-herrmann/clean_scirepeval huggingface dataset:
Video coding with flexible MCTF structures for low end-to-end delay

Some of the most powerful schemes for scalable video coding are based on the so-called (t + 2D) paradigm and provide coding performance competitive with state-of-the-art codecs. However, the temporal multiresolution schemes used in such codecs introduce a non-negligible delay, preventing their use in applications which require low latency, like video conferencing or video streaming. In this paper, we provide a flexible approach to reduce the end-to-end delay involved in motion-compensated temporal filtering schemes. We show by simulations results the trade-offs observed between coding efficiency and low end-to-end delay.
Intra-frame prediction with lapped transforms for image coding

In this paper we propose the use of intra-frame prediction with lapped transforms for image coding. Both lapped transforms and intra prediction exploit the redundancies of neighboring blocks and the combination of the two techniques results in a very efficient image coding scheme. The difficulty to combine them comes from the necessity to use for the prediction of the current block pixels in the causal neighborhood that have not been completely processed by the overlapping transform. We show how to overcome this difficulty, and thus the system presented here outperforms the traditional one, using intra-prediction and DCT, and the direct application of lapped transforms in all tested images.
A differential motion estimation method for image interpolation in distributed video coding

Motion estimation methods based on differential techniques proved to be very useful in the context of video analysis, but have a limited employment in classical video compression because, though accurate, the dense motion vector field they produce requires too much coding resource and computational effort. On the contrary, this kind of algorithm could be useful in the framework of distributed video coding (DVC). In this paper we propose a differential motion estimation algorithm which can run at the decoder in a DVC scheme, without requiring any increase in coding rate. This algorithm allows a performance improvement in image interpolation with respect to state-of-the-art algorithms.
Image interpolation with edge-preserving differential motion refinement

Motion estimation (ME) methods based on differential techniques provide useful information for video analysis, and moreover it is relatively easy to embed into them regularity constraints enforcing for example, contour preservation. On the other hand, these techniques are rarely employed for video compression since, though accurate, the dense motion vector field (MVF) they produce requires too much coding resource and computational effort. However, this kind of algorithm could be useful in the framework of distributed video coding (DVC), where the motion vector are computed at the decoder side, so that no bit-rate is needed to transmit them. Moreover usually the decoder has enough computational power to face with the increased complexity of differential ME. In this paper we introduce a new image interpolation algorithm to be used in the context of DVC. This algorithm combines a popular DVC technique with differential ME. We adapt a pel-recursive differential ME algorithm to the DVC context; moreover we insert a regularity constraint which allows more consistent MVFs. The experimental results are encouraging: the quality of interpolated images is improved of up to 1.1 dB w.r.t. to state-of-the-art techniques. These results prove to be consistent when we use different GOP sizes.
Joint source-channel coding of scalable video

We present a joint source-channel coding scheme developed for transmitting scalable video over a noisy channel. This scheme is based on a 2D+t motion-compensated wavelet decomposition of the video sequence, on a vector quantization and on an optimal linear labelling. In P. Knagenhjelm and E. Agrell's work ( 1996), it is shown that on binary discrete channels, the channel distortion is minimized by linear labelling. In this paper, we provide a vector quantization method dependent on a linear labelling which minimizes not only the channel distortion but at the same time the distortion of our video source. Moreover, by exploiting a statistical model of the spatio-temporal wavelet coefficients we adapt the vector quantizer to the non-stationarity of the source. Simulation results demonstrate the robustness of this scheme under noisy channel conditions.
Adaptive lifting schemes combining seminorms for lossless image compression

We present a new class of adaptive wavelet decompositions that can capture the directional nature of picture information. Our method exploits the properties of seminorms to build lifting structures able to choose between different update filters, the choice being triggered by a local gradient of the input. In order to discriminate between different geometrical information, the system makes use of multiple criteria, giving rise to multiple choice of update filters. We establish the conditions under which these decisions can be recovered at synthesis, without the need for transmitting overhead information.
Contour-Based Depth Coding: A Subjective Quality Assessment Study

Multi-view video plus depth is emerging as the most flexible format for 3D video representation, as witnessed by the current standardization efforts by ISO and ITU. The depth information allows synthesizing virtual view points, and for its compression various techniques have been proposed. It is generally recognized that a high quality view rendering at the receiver side is possible only by preserving the contour information since distortions on edges during the encoding step would cause a sensible degradation on the synthesized view and on the 3D perception. As a consequence recent approaches include contour-based coding of depths. However, the impact of contour-preserving depth-coding on the perceived quality of synthesized images has not been conveniently studied. Therefore in this paper we make an investigation by means of a subjective study to better understand the limits and the potentialities of the different techniques. Our results show that the contour information is indeed relevant in the synthesis step: preserving the contours and coding coarsely the rest typically leads to images that users cannot tell apart from the reference ones, even at low bit rate. Moreover, our results show that objective metrics that are commonly used to evaluate synthesized images may have a low correlation coefficient with MOS rates and are in general not consistent across several techniques and contents.
H.264-based multiple description coding using motion compensated temporal interpolation

Multiple description coding is a framework adapted to noisy transmission environments. In this work, we use H.264 to create two descriptions of a video sequence, each of them assuring a minimum quality level. If both of them are received, a suitable algorithm is used to produce an improved quality sequence. The key technique is a temporal image interpolation using motion compensation, inspired to the distributed video coding context. The interpolated image blocks are weighted with the received blocks obtained from the other description. The optimal weights are computed at the encoder and efficiently sent to the decoder as side information. The proposed technique shows a remarkable gain for central decoding with respect to similar methods available in the state of the art.
An adaptive synthesis filter bank for image decoding with fractional scalability

Transform image coding and more particularly the subclass of block based transformations are widely used to compress images. The JPEG standard for still images and MPEG codec specifications for video are very efficient implementations, but these algorithms perform image reconstruction without taking into account the quantization operations performed on the transform coefficients. In this paper, we propose an adaptive algorithm to tune the inverse transformation matrix as a function of the quantization level in order to minimize the reconstruction error. The developed algorithm provides quality scalability features and also integrates resizing operations into the inverse transformation process leading to a spatial scalability of fractional factors.
Modified M-band synthesis filter bank for fractional scalability of images

Subband image coding with classical wavelet transforms inherently provides dyadic scalability since the low-pass approximation band can be used as a half-sized version of the original image. The multiresolution approach further provides scalability factors that are powers of two, but no other factors can be easily reached. On the other hand, a hierarchical scheme based on M-bandtransforms will lead only to scalability factors that are powers of M. In this letter, we present how an M-band synthesis filter bank can be modified in order to directly reconstruct an output image of a resolution reduced by a factor M/P, where P is an integer lower than M. As the analysis filter bank is not modified, this technique allows the design of subband decoding schemes that support fractional scalability. With this feature, the same compressed bitstream can be directly decoded by different receivers at several resolutions, depending on their computational and bandwith capabilities.
An epigraphical convex optimization approach for multicomponent image restoration using non-local structure tensor

TV-like constraints/regularizations are useful tools in variational methods for multicomponent image restoration. In this paper, we design more sophisticated non-local TV constraints which are derived from the structure tensor. The proposed approach allows us to measure the non-local variations, jointly for the different components, through various ℓ1,p matrix norms with p ≥ 1. The related convex constrained optimization problems are solved through a novel epigraphical projection method. This formulation can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments carried out for color images demonstrate the interest of considering a Non-Local Structure Tensor TV and show that the proposed epigraphical projection method leads to significant improvements in terms of convergence speed over existing numerical solutions.
A model-based error concealment method for scalable video

Scalable video compression algorithms based on motion-compensated spatio-temporal wavelet decomposition have recently gained a lot of interest for video transmission over packet networks due to their flexibility and coding performance. Meanwhile, the adaptation of scalable video to the variations of the transmission conditions over the network could result in losing a part of the bitstream. In this paper we show that a statistical spatio-temporal model of the wavelet coefficients can be effective in the prediction of the lost subbands at the decoder side. Based on this analysis we propose an error concealment method and provide experimental results showing the quality improvement that can be achieved by using it.
Objective evaluation of compressed video's temporal flickering

This paper proposes a new method to evaluate the temporal impairments of compressed video signals. The proposed algorithm is block based; each block in a given frame is first classified as stable or in motion with respect to its co-located blocks in the previous and next frames. For each in-motion block, a motion estimation process is used to find its best match. Then the flickering estimation is conducted over a 3D window involving the current block and its temporally co-located or best matched blocks of the previous and next frames. The temporal quality is finally deduced from the flickering estimation. Simulation results show the efficacy of our algorithm.
On the Uniform Quantization of a Class of Sparse Sources

We consider the uniform scalar quantization of a class of mixed distributed memoryless sources, namely sources having a Bernoulli Generalized Gaussian (BGG) distribution. Both for low and high resolutions, asymptotic expressions of the distortion for a pth-order moment error measure, and close approximations of the entropy are provided for these sources. Operational rate-distortion functions at high bit rate and their slope factors at low bit rate are derived. The dependence of these results on p and the distribution parameters as well as the relation to the Shannon optimal rate-distortion bound are then discussed. The application of these results to transform coding in two simple cases is finally highlighted.
3D, 3-band, 3-tap temporal lifting for scalable video coding

Recently, a new generation of video compression techniques gained the interest of the research engineering community, by proposing state-of-the-art compression performance in addition to temporal/spatial/SNR scalability features. They exploit interframe redundancy through a temporal wavelet transform in the motion direction. While classical two-band decompositions offer dyadic factors of scalability, in this paper we present a three-band temporal lifting structure providing a factor 3 of scalability in a motion-compensated subband video coding scheme. More flexibility is thus available in order to adapt the bitstream to the bandwidth variations in heterogeneous networks. We also show improved compression performance compared with a dyadic Haar MCTF analysis.
A new object based quality metric based on SIFT and SSIM

We propose a full reference visual quality metric to evaluate a semantic coding system which may not preserve exactly the position and/or the shape of objects. The metric is based on Scale-Invariant Feature Transform (SIFT) points. More specifically, Structural SIMilarity (SSIM) on windows around the SIFT points measures the compression artifacts (SSIM_SIFT). Conversely, the standard deviation of the matching distance between the SIFT points measures the geometric distortion (GEOMETRIC_SIFT). We validate our metric with subjective evaluation and reach a Spearman correlation of 0.86 for SSIM_SIFT and 0.74 for GEOMETRIC_SIFT.
Video Coding with Wavelet-Domain Conditional Replenishment and Unequal Error Protection

A simple and computationally lightweight video coder employing shape-adaptive, embedded intraframe coding and wavelet-domain conditional replenishment is proposed. Robustness to packet losses arises from packetization of the embedded bitstream with unequal error protection which is assigned to the packets with a fast, locally optimal procedure. Experimental results reveal that, when compared to H.264/AVC configured for low-complexity, error-resilient operation, not only does the proposed coder usually produce substantially superior rate-distortion performance as packet losses increase, it also achieves a significantly faster encoding speed.
Bit allocation algorithm for joint source-channel coding of t + 2D video sequences

A joint source-channel coding scheme of t + 2D decomposed video sequences and an iterative bit allocation are presented. The joint source-channel coding scheme consists of a vector quantization and a linear labelling by lattice constellations minimizing simultaneously the channel and the source distortion. The channel distortion, due to the linear labelling, is minimized and depends on the value of the noise variance and the variance of the source. The iterative algorithm results in an optimal codebook allocation subject to a global bit rate and a nonnegativity constraint. The overall flexible coding scheme is proved to be very efficient in noisy environments.
Three-dimensional lifting schemes for motion compensated video compression

Three-dimensional wavelet decompositions are efficient tools for scalable video coding. We show a lifting formulation for these decompositions. The temporal wavelet transform is inherently nonlinear, due to the motion estimation step, and the lifting formalism allows us to provide several improvements to the scheme initially proposed by Choi and Woods: a better processing of the uncovered areas is proposed and an overlapped motion-compensated temporal filtering method is introduced in the multiresolution decomposition. As shown by simulations, the proposed method results in higher coding efficiency, while keeping the scalability functionalities.
Two-dimensional non separable adaptive lifting scheme for still and stereo image coding

Many existing works related to lossy-to-lossless image compression are based on the lifting concept. However, it has been observed that the separable lifting scheme structure presents some limitations because of the separable processing performed along the image lines and columns. In this paper, we propose to use a 2D non separable lifting scheme decomposition that enables progressive reconstruction and exact decoding of images. More precisely, we focus on the optimization of all the involved decomposition operators. In this respect, we design the prediction filters by minimizing the variance of the detail signals. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artefacts. Simulations carried out on still and stereo images show the benefits which can be drawn from the proposed optimization of the lifting operators.
Quantization of adaptive wavelets for image compression

In this paper we investigate the potential of a special class of adaptive wavelet schemes to yield an effective representation for compression purposes. In particular, we analyze the effect of a scalar uniform quantization in an adaptive multiresolution analysis based on a lifting implementation. We provide conditions for recovering the original decisions at synthesis and we provide expressions that relate the reconstruction error to the quantization error. Such an analysis is essential for the application of our adaptive decompositions in lossy image compression algorithms.
On a practical approach to source separation over finite fields for network coding applications

In Blind Source Separation, or BSS, a set of source signals are recovered from a set of mixed observations without knowledge of the mixing parameters. Originated for real signals, BSS has recently been applied to finite fields, enabling more practical applications. However, classical entropy-based techniques do not perform well in finite fields. Here, we propose a non-linear encoding of the sources to increase the discriminating power of the separation methods. Our results show that the encoding improves the success rate of the separation for sources with few samples in large finite fields, both conditions met in practical networking applications. Our results open new possibilities in the context of network coding-wherein linear combinations of packets are sent in order to maximize throughput and increase loss immunity- by relieving the nodes from the need to send the combination coefficients, thus reducing the overhead cost.
Fusion of Global and Local Motion Estimation Using Foreground Objects for Distributed Video Coding

The side information (SI) in Distributed Video Coding (DVC) is estimated using the available decoded frames and exploited for the decoding and reconstruction of other frames. The quality of the SI has a strong impact on the performance of DVC. Here, we propose a new approach that combines both global and local SI to improve coding performance. Since the background pixels in a frame are assigned to global estimation and the foreground objects to local estimation, one needs to estimate foreground objects in the SI using the backward and forward foreground objects, the background pixels are directly taken from the global SI. Specifically, elastic curves and local motion compensation are used to generate the foreground objects masks in the SI. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.39 dB for a group of picture (GOP) size of 2, and up to 4.73 dB for larger GOP sizes, with respect to the reference DISCOVER codec.
Rate-distortion results for Generalized Gaussian distributions

In this paper, we provide operational rate-distortion results for memoryless generalized Gaussian sources. Close approximations of the entropy are provided for these sources, after a uniform scalar quantization at low/high resolution. Asymptotic expressions of the distortion for an arbitrary p-th order error measure are also given. The resulting approximations at low/high bitrate of the operational rate-distortion function are thus compared with the Shannon optimal bound showing the overall good performance of uniform quantization rules.
Versatile multiview layered video based on distributed source coding

Video content constitutes today a large part of the data traffic on the Internet. This is allowed by the capillary spreading of video codec technologies: nowadays, every computer, tablet and smart phone is equipped with video encoding and decoding technologies. As a matter of fact, the video content often exists in different formats, that, even though they can be incompatible to each other, still have a significant mutual redundancy. The incompatibility prevents an efficient exploitation of the scalability, which on the other hand is a very important characteristic when it comes to efficient network use. An interesting alternative to classical scalable video is to use distributed video coding (DVC) for the enhancement layers. In the envisaged scenario, clients have different decoders for the base layer, adapted to the characteristics of their de-vice. However they can share the same enhancement layer, since DVC allows encoding frames independently from the reference that will be employed at the decoder. This approach has been considered in the past in order to improve temporal and spatial scalability. In this work we review the existing approaches, improve them using more recent DVC techniques and perform a new analysis for the emerging multi-view applications.
3-band motion-compensated temporal structures for scalable video coding

Recent breakthroughs in motion-compensated temporal wavelet filtering have finally enabled implementation of highly efficient scalable and error-resilient video codecs. These new wavelet codecs provide numerous advantages over nonscalable conventional solutions techniques based on motion-compensated prediction, such as no recursive predictive loop, separation of noise and sampling artifacts from the content through use of longer temporal filters, removal of long range as well as short range temporal redundancies, etc. Moreover, these wavelet video coding schemes can provide flexible spatial, temporal, signal-to-noise ratio and complexity scalability with fine granularity over a large range of bit rates, while maintaining a very high coding efficiency. However, most motion-compensated wavelet video schemes are based on classical two-band decompositions that offer only dyadic factors of temporal scalability. In this paper, we propose a three-band temporal structure that extends the concept of motion-compensated temporal filtering (MCTF) that was introduced in the classical lifting framework. These newly introduced structures provide higher temporal scalability flexibility, as well as improved compression performance compared with dyadic Haar MCTF
Wavelet-based multiple description coding of images with iterative convex optimization techniques

We consider the problem of image transmission on error-prone networks with little or no error protection. To this end, we build a multiple description scheme based on classical biorthogonal filter banks, that achieves good reconstruction even at low bitrates. The novelty of the proposed approach mainly consists in building a 2D wavelet frame representation with low redundancy. Another contribution of this work is the use of a convex optimization approach at the decoder end in order to best take advantage of all the received information. The quantization constraints define convex sets, which allow us to apply fast iterative projection techniques to find a feasible solution of the decoding problem.