nils-herrmann/clean_scirepeval huggingface dataset:
OpenCL implementation of motion estimation for cloud video processing

With the raise of cloud computing infrastructures on one side and the increased accessibility of parallel computational devices on the other, such as GPUs and multi-core CPUs, parallel programming has recently gained a renewed interest. This is particularly true in the domain of video coding, where the complexity and time consumption of the algorithms tend to limit the access to the core technology. In this work, we focus on the motion estimation problem, well-known to be the most time consuming step of a majority of video coding techniques. By relying on the use of the OpenCL standard, which provides a cross-platform framework for parallel programming, we propose here a scalable CPU/GPU implementation of the full search motion estimation algorithm (FSBM), and study its performances also with respect to the issues raised by the use of OpenCL.
Vector Lifting Schemes for Stereo Image Coding

Many research efforts have been devoted to the improvement of stereo image coding techniques for storage or transmission. In this paper, we are mainly interested in lossy-to-lossless coding schemes for stereo images allowing progressive reconstruction. The most commonly used approaches for stereo compression are based on disparity compensation techniques. The basic principle involved in this technique first consists of estimating the disparity map. Then, one image is considered as a reference and the other is predicted in order to generate a residual image. In this paper, we propose a novel approach, based on vector lifting schemes (VLS), which offers the advantage of generating two compact multiresolution representations of the left and the right views. We present two versions of this new scheme. A theoretical analysis of the performance of the considered VLS is also conducted. Experimental results indicate a significant improvement using the proposed structures compared with conventional methods.
Successive refinement of side information using adaptive search area for long duration GOPs in distributed video coding

In distributed video coding, the reference frames are used to generate a side information at the decoder in order to decode the Wyner-Ziv frame. The quality of side information has a strong impact on the coding efficiency of distributed video coding. The estimation of the side information becomes less accurate when the temporal distance between the neighboring reference frames increases or when the sequence contains fast motion. In this paper, we propose a new method based on successive refinement of the side information by adapting the motion search area after decoding the first DCT band. More specifically, different search areas are initially set, according to the temporal distance between the neighboring reference frames. Furthermore, the size of the search area is adapted to the motion content after decoding the first DCT band by using the partially decoded Wyner-Ziv frame. This adapted search area is used in order to refine the side information after decoding each remaining DCT band. The experimental results show that the proposed technique allows an improvement in rate distortion performance that can reach 0.7 dB for a GOP size of 8, compared to the method where a constant search area is used, and a significant gain up to 3.23 dB, with respect to DISCOVER codec. The improvement in the quality of the final side information reaches 5.6 dB for some frames, compared to constant search area. Moreover, the proposed method reduces significantly the decoding time for all test sequences.
A Bit Allocation Method for Sparse Source Coding

In this paper, we develop an efficient bit allocation strategy for subband-based image coding systems. More specifically, our objective is to design a new optimization algorithm based on a rate-distortion optimality criterion. To this end, we consider the uniform scalar quantization of a class of mixed distributed sources following a Bernoulli-generalized Gaussian distribution. This model appears to be particularly well-adapted for image data, which have a sparse representation in a wavelet basis. In this paper, we propose new approximations of the entropy and the distortion functions using piecewise affine and exponential forms, respectively. Because of these approximations, bit allocation is reformulated as a convex optimization problem. Solving the resulting problem allows us to derive the optimal quantization step for each subband. Experimental results show the benefits that can be drawn from the proposed bit allocation method in a typical transform-based coding application.
An MDC-based video streaming architecture for mobile networks

Multiple description coding (MDC) is a framework designed to improve the robustness of video content transmission in lossy environments. In this work, we propose an MDC technique using a legacy coder to produce two descriptions, based on separation of even and odd frames. If only one description is received, the missing frames are reconstructed using temporal high-order motion interpolation (HOMI), a technique originally proposed for distributed video coding. If both descriptions are received, the frames are reconstructed as a block-wise linear combination of the two descriptions, with the coefficient computed at the encoder in a RD-optimised fashion, encoded with a context-adaptive arithmetic coder, and sent as side information. We integrated the proposed technique in a mobile ad-hoc streaming protocol, and tested it using a group mobility model. The results show a non-negligible gain for the expected video quality, with respect to the reference technique.
Adaptive update lifting with a decision rule based on derivative filters

This letter treats a class of adaptive update-lifting schemes that do not require bookkeeping for perfect reconstruction. The choice of the update-lifting filter is triggered by a binary threshold criterion based on a generalized gradient that is chosen in such a way that it only smooths homogeneous regions. This criterion can be chosen so that it ignores portions of a signal that are polynomial up to a given order. The update-lifting filter modifies the signal in these polynomial regions but leaves other portions unaffected.
A convex programming approach for color stereo matching

This paper addresses the problem of dense disparity estimation from a pair of color stereo images. Based on a convex set theoretic formulation, the stereo matching problem is cast as a convex programming problem in which a color-based objective function is minimized under specific convex constraints. These constraints arise from prior knowledge and rely on various properties of the disparity field to be estimated. The resulting multi-constrained optimization problem is solved via an efficient parallel block-iterative algorithm. Four different color spaces have been tested in order to evaluate their suitability for stereo matching. Experiments on standard stereo images show that the matching results have been efficiently improved when using color information instead of grey values.
A distributed video coding system for multi view video plus depth

Multi-view video plus depth (MVD) is gathering huge attention, as witnessed by the recent standardization activity, since its rich information about the geometry of the scene allows high-quality synthesis of virtual viewpoints. Distributed video coding of such kind of content is a challenging problem whose solution could enable new services as interactive multi-view streaming. In this work we propose to exploit the geometrical information of the MVD format in order to estimate inter-view occlusions without communication among cameras. Experimental results show a bit rate reduction up to 77% for low bit rate w.r.t. state-of-the-art architectures.
Dynamic rate allocation with variable quantization in multi-sensor Wyner-Ziv video coding systems

In this paper, we present a novel algorithm for dynamic quantization in distributed Wyner-Ziv video coding. In contrast with previous work where the quantization parameter is fixed and a feedback channel is used, our proposed technique relies on theoretical calculations to jointly determine the number of quantization levels along with a suitable compression rate for each video frame. It employs a cross-layer approach that dynamically allocates unequal transmission rates for different users by taking into account the amount of motion in the captured video scenes on one hand and the transmission conditions for each sensor on the other. The application of this algorithm in a wireless video sensor network shows a significant improvement in the system performance when compared to a traditional system that allocates equal channel resources with a fixed quantization parameter.
Side information refinement for long duration GOPs in DVC

Side information generation is a critical step in distributed video coding systems. This is performed by using motion compensated temporal interpolation between two or more key frames (KFs). However, when the temporal distance between key frames increases (i.e. when the GOP size becomes large), the linear interpolation becomes less effective. In a previous work we showed that this problem can be mitigated by using high order interpolation. Now, in the case of long duration GOP, state-of-the-art algorithms propose a hierarchical algorithm for side information generation. By using this procedure, the quality of the central interpolated image in a GOP is consistently worse than images closer to the KFs. In this paper we propose a refinement of the central WZFs by higher order interpolation of the already decoded WZFs, that are closer to the WZF to be estimated. So we reduce the fluctuation of side information quality, with a beneficial impact on final rate-distortion characteristics of the system. The experimental results show an improvement on the SI up to 2.71 dB with respect the state-of-the-art and a global improvement of the PSNR on the decoded frames up to 0.71 dB and a bit rate reduction up to 15 %.
TCP and network coding equilibrium and dynamic properties

This paper analyzes the impact on the TCP-Reno congestion control mechanism of a network coding (NC) layer inserted in the TCP/IP stack. A multi-source multi-link model is considered to study the equilibrium and dynamic properties of the TCP-NC protocol with RED as active queue management mechanism. The existence and uniqueness of some network equilibrium is demonstrated and characterized in terms of average throughput, loss rate, and queue length. Global stability is proved in absence of forward delay, and the effects of the NC redundancy factor and of the delay on the local stability of TCP-NC-RED is studied around the equilibrium point. Results provided by the proposed model are compared to those obtained by simulation for N sources sharing a single link. TCP-NC-RED becomes unstable when delay or capacity increases, as TCP-Reno does. Its stability region is characterized as a function of the redundancy factor.
Disparity-compensated compressed-sensing reconstruction for multiview images

In a multiview-imaging setting, image-acquisition costs could be substantially diminished if some of the cameras operate at a reduced quality. Compressed sensing is proposed to effectuate such a reduction in image quality wherein certain images are acquired with random measurements at a reduced sampling rate via projection onto a random basis of lower dimension. To recover such projected images, compressed-sensing recovery incorporating disparity compensation is employed. Based on a recent compressed-sensing recovery algorithm for images that couples an iterative projection-based reconstruction with a smoothing step, the proposed algorithm drives image recovery using the projection-domain residual between the random measurements of the image in question and a disparity-based prediction created from adjacent, high-quality images. Experimental results reveal that the disparity-based reconstruction significantly outperforms direct reconstruction using simply the random measurements of the image alone.
Proximal splitting methods for depth estimation

Stereo matching is an active area of research in image processing. In a recent work, a convex programming approach was developed in order to generate a dense disparity field. In this paper, we address the same estimation problem and propose to solve it in a more general convex optimization framework based on proximal methods. More precisely, unlike previous works where the criterion must satisfy some restrictive conditions in order to be able to numerically solve the minimization problem, this work offers a great flexibility in the choice of the involved criterion. The method is validated in a stereo image coding framework, and the results demonstrate the good performance of the proposed parallel proximal algorithm.
Sparse based adaptive non separable vector lifting scheme for holograms compression

Digital holography offers appealing features for microscopic and 3D imaging applications. Thus, the growing interest of digital holographic data has led to the constitution of huge amounts of data. Therefore, in order to efficiently store and transmit this data, the design of hologram compression schemes becomes a crucial issue. In this paper, we propose to encode two sets of data, referred to as shifted distance information, by applying a non separable vector lifting scheme. The idea consists in exploiting the redundancies existing between the two sets and building a content-adaptive hologram compression scheme by resorting to a sparse optimization technique. Experimental results show that the proposed method outperforms the state-of-the-art ones.
Redundant multiresolution analysis for multiple description video coding

Multiple description coding (MDC) is a joint source-channel coding technique specifically designed for real-time multimedia applications over best effort switched packet networks (such as Internet), in order to cope with packet losses due to transmission errors or network congestion. Error resilience of transmitted bitstreams is thus significantly increased, but this does not solve problems like bitstream adaptation to bandwidth variations or receiver characteristics, which are in turn addressed by scalable coding techniques. In this paper, we present a new method of multiple description coding of scalable video, combining the scalability features with MDC. We propose a redundant motion-compensated temporal scheme related to Haar multiresolution analysis. We also present an equivalent lifting implementation leading to simple central and lateral decoders.
Epigraphical proximal projection for sparse multiclass SVM

Sparsity inducing penalizations are useful tools in variational methods for machine learning. In this paper, we design a learning algorithm for multiclass support vector machines that allows us to enforce sparsity through various nonsmooth regularizations, such as the mixed ℓ1, p-norm with p ≥ 1. The proposed constrained convex optimization approach involves an epigraphical constraint for which we derive the closed-form expression of the associated projection. This sparse multiclass SVM problem can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments carried out for handwritten digits demonstrate the interest of considering nonsmooth sparsity-inducing regularizations and the efficiency of the proposed epigraphical projection method.
Multiple description video coding and iterative decoding of LDPCA codes with side information

In this paper, we propose the use of multiple description coding to increase the robustness of distributed video coding while keeping good rate-distortion performance. The video sequence is structured into key frames and Wyner-Ziv frames. For each type of frame, two descriptions are generated by a multiple description scalar quantizer and sent on a loss-prone channel. When both Wyner-Ziv descriptions are received, they are jointly decoded along with the side information. We investigate the influence of the amount of redundancy and of the iterative decoding of the descriptions on the performance.
Extended Selective Encryption of H.264/AVC (CABAC)- and HEVC-Encoded Video Streams

This paper proposes an extended selective encryption (SE) method for both H.264/advanced video coding (AVC) (CABAC) and High Efficiency Video Coding (HEVC) streams, addressing the main security issue that SE is facing: content protection, related to the amount of information leakage through a protected video. Our contribution is the improvement in the visual distortion induced by SE approaches. Previous works on both H.264/AVC (CABAC) and HEVC limit encryption to bins treated by one specific mode of CABAC—its bypass mode—which has the advantage of preserving the overall bitrate, we propose here to also rely on the encryption of the more widely used mode of CABAC—its regular mode. This allows encryption of a major codeword for video reconstruction, the prediction modes for intra blocks/units. Disturbing their statistics may cause bitrate overhead, which is the tradeoff for improving the content security level of the SE approach. A comprehensive study of this compromise between the improvement in the scrambling efficiency and the undesirable aftereffects is presented in this paper, and a specific security analysis of the proposed CABAC regular mode encryption is conducted.
Vector quantization for computer generated phase-shifting holograms

Concerning the reconstruction quality, phase-shifting scheme has been proved to be efficient for digital holography. As a benefit from three differently phased reference waves, the reconstructed object image does no longer suffer from the zero-order image or twin image. In order to reduce the obvious storage and transmission burden brought in by extra holograms, in this paper, vector quantization (VQ) based on LGB algorithm (LBG-VQ) for computer generated phase-shifting holograms (CGPSHs) is proposed. The interference patterns at the hologram plane is generated by Fresnel transformation in phase-shifting digital holography (PSDH) scheme. Meanwhile, the performance of adaptive scalar quantization using Lloyd Max algorithm on effective holographic information is also investigated. Simulation results indicate that LBG-VQ outperforms adaptive scalar Lloyd Max quantization.
Robust decoding of a 3D-ESCOT bitstream transmitted over a noisy channel

In this paper, we propose a joint source-channel (JSC) decoding scheme for 3D ESCOT-based video coders, such as Vidwav. The embedded bitstream generated by such coders is very sensitive to transmission errors unavoidable on wireless channels. The proposed JSC decoder employs the residual redundancy left in the bitstream by the source coder combined with bit reliability information provided by the channel or channel decoder to correct transmission errors. When considering an AWGN channel, the performance gains are in average 4 dB in terms of PSNR of the reconstructed frames, and 0.7 dB in terms of channel SNR. When considering individual frames, the obtained gain is up to 15 dB in PSNR.
A comparison of multispectral satellite sequence compression approaches

Several techniques have been proposed in the last few years for the compression or hyperspectral and multispectral images. All of them tried to exploit, in various degrees, the particularities of these images, especially of the intraband and interband redundancies they exhibit. In this paper, the authors considered the compression of multitemporal and multispectral images and made a comparison between still image and video approaches for the compression of these images acquired by SPOT 1, 2 and 4. The still image compression is realized using the state-of-the-art JPEG2000 codec, while the video one uses an EZBC wavelet-based codec. Both spectral and temporal decorrelation approaches are discussed.
Joint source-channel coding/decoding of 3D-ESCOT bitstreams

Joint source-channel decoding (JSCD) exploits residual redundancy in compressed bitstreams to improve the robustness to transmission errors of multimedia coding schemes. This paper proposes an architecture to introduce some additional side information in compressed streams to help JSCD. This architecture exploits a reference decoder already present or introduced at the encoder side. An application to the robust decoding of 3D-ESCOT encoded bitstreams generated within the Vidwav video coder is presented. The layered bitstream generated by this encoder allows SNR scalability, and moreover, when processed by a JSCD, provides increased robustness to transmission errors compared with a single layered bitstream.
Depth map coding by dense disparity estimation for MVD compression

In multiview video representation, one of the most popular format is the so-called multiple view video plus depth. This representation is made up of N image sequences, each accompanied by a sequence of depth maps, telling the distance of each represented pixel from the observing camera. The depth maps are needed at the decoder side in order to generate intermediate views and therefore to enrich the user experience. This format is very flexible but also very demanding, in terms of storage space or and transmission bandwidth. Therefore, compression is needed. At this end, one of the key steps is an efficient representation of depth maps. In this work we build over a proposed method for multiple view video coding, based on dense disparity estimation between views. This allows us to obtain a compact and high-quality depthmap representation. In particular we explore the complex relationship between estimation and encoding parameters, showing that an optimal parameter set exist, that allows a fine-tuning of the estimation phase and an adaption of its results to the subsequent compression phase. Experiments are encouraging, showing remarkable gain over simple methods such as H.264/AVC simulcast, and even some gain with respect to more sophisticated techniques such as MVC.
Image interpolation with dense disparity estimation in multiview distributed video coding

This paper deals with the side information (SI) generation problem in multiview distributed video coding (MDVC). For inter-view interpolation, we propose a novel dense disparity estimation (DE) approach combined with a popular distributed video coding (DVC) technique. As disparity vectors are computed at the decoder side, and no coding rate is needed to transmit them, dense estimation techniques are made possible, leading to improved results without requiring high coding resources. Experimental results show that the proposed interpolation technique can achieve up to 2.0 dB improvement in SI reconstruction performance, when compared to state-of-the-art DVC techniques.
An efficient weight optimization algorithm for image representation using nonorthogonal basis images

Though image coding techniques that employ subsets of nonorthogonal basis images chosen from two or more transform domains have been shown consistently to yield higher image quality than those based on one transform for a fixed compression ratio, they have not been widely employed due to their very high computational complexity of existing realization approaches. This paper presents a new realization approach for mixed-transform image representation. Computational complexity can be greatly reduced compared with existing approaches.
Restoring halftoned color-quantized image with genetic algorithm

Restoration of color quantized images is rarely addressed in the literature especially when the images are color quantized with halftoning. Conventional color image restoration algorithms are not suitable for handling this problem. A new restoration algorithm based on a genetic algorithm is presented in this paper. This algorithm makes use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results showed that it was able to provide a very promising restoration result.