nils-herrmann/clean_scirepeval huggingface dataset:
Halftone Image Watermarking by Content Aware Double-Sided Embedding Error Diffusion

In this paper, we carry out a performance analysis from a probabilistic perspective to introduce the error diffusion-based halftone visual watermarking (EDHVW) methods’ expected performances and limitations. Then, we propose a new general EDHVW method, content aware double-sided embedding error diffusion (CaDEED), via considering the expected watermark decoding performance with specific content of the cover images and watermark, different noise tolerance abilities of various cover image content, and the different importance levels of every pixel (when being perceived) in the secret pattern (watermark). To demonstrate the effectiveness of CaDEED, we propose CaDEED with expectation constraint (CaDEED-EC) and CaDEED-noise visibility function (NVF) and importance factor (IF) (CaDEED-N&I). Specifically, we build CaDEED-EC by only considering the expected performances of specific cover images and watermark. By adopting the NVF and proposing the IF to assign weights to every embedding location and watermark pixel, respectively, we build the specific method CaDEED-N&I. In the experiments, we select the optimal parameters for NVF and IF via extensive experiments. In both the numerical and visual comparisons, the experimental results demonstrate the superiority of our proposed work.
Rate-Constrained 3D Surface Estimation From Noise-Corrupted Multiview Depth Videos

Transmitting compactly represented geometry of a dynamic 3D scene from a sender can enable a multitude of imaging functionalities at a receiver, such as synthesis of virtual images at freely chosen viewpoints via depth-image-based rendering. While depth maps-projections of 3D geometry onto 2D image planes at chosen camera viewpoints-can nowadays be readily captured by inexpensive depth sensors, they are often corrupted by non-negligible acquisition noise. Given depth maps need to be denoised and compressed at the encoder for efficient network transmission to the decoder, in this paper, we consider the denoising and compression problems jointly, arguing that doing so will result in a better overall performance than the alternative of solving the two problems separately in two stages. Specifically, we formulate a rate-constrained estimation problem, where given a set of observed noise-corrupted depth maps, the most probable (maximum a posteriori (MAP)) 3D surface is sought within a search space of surfaces with representation size no larger than a prespecified rate constraint. Our rate-constrained MAP solution reduces to the conventional unconstrained MAP 3D surface reconstruction solution if the rate constraint is loose. To solve our posed rate-constrained estimation problem, we propose an iterative algorithm, where in each iteration the structure (object boundaries) and the texture (surfaces within the object boundaries) of the depth maps are optimized alternately. Using the MVC codec for compression of multiview depth video and MPEG free viewpoint video sequences as input, experimental results show that rate-constrained estimated 3D surfaces computed by our algorithm can reduce coding rate of depth maps by up to 32% compared with unconstrained estimated surfaces for the same quality of synthesized virtual views at the decoder.
Content based fast prediction unit quadtree depth decision algorithm for HEVC

The nested quadtree based partitioning scheme of HEVC contributes a lot to the coding efficiency improvement, however, it adds significant complexity to the encoder. This paper introduces a fast prediction unit (PU) level quadtree depth decision (FPDD) algorithm. It is achieved by making use of the inherited correlation of PU quadtree structure between current largest coding unit (LCU) and its spatial and temporal neighbors. To reduce error propagation, we also propose a confidence grading scheme to prevent LCUs with bad prediction from being referred to by others. Results show that our proposed algorithm provides averagely 20.0% (up to 39.3%) encoding time reduction whilst causing negligible RD performance loss (0.2% BD-Rate increase on average) compared with HM 7.0.
Fast and efficient intra-frame deinterlacing using observation model based bilateral filter

Recently, a few bilateral filter based interpolation and intraframe deinterlacing algorithms have been proposed, but these algorithms only use prior information (bilateral filter). In this paper, we propose an efficient and fast intra-frame deinterlacing algorithm using an observation model based bilateral filter (using both likelihood and prior information). Our proposed algorithm is also able to use approximated horizontal pixels for the deinterlacing, which results into the better prediction of the edges. From extensive experiments, it is observed that the proposed algorithm has the capability of provide satisfactory results in terms of both objective and subjective quality.
Error recovery of variable length code over BSC with arbitrary crossover probability

The error recovery capability of variable length code (VLC) has been considered as an important performance and design criterion in addition to its coding efficiency. However, almost all of the existing methods for evaluating the error recovery capability of VLC assume that the transmission fault is a random single bit inversion. In this paper, we consider a more generalized problem of precisely evaluating the error recovery capability of VLC in the case that the encoded bit stream is transmitted over a BSC with arbitrary crossover probability. By making use of the Perron-Frobenius Theorem, we derive a very simple expression for the exact mean error propagation rate (MEPR), and show that the variance of error propagation rate (VEPR) is zero. We also prove that in the regime of very low crossover probability, the mean error propagation length (MEPL) derived for single inversion error case approaches a scaled value of the MEPR. Furthermore, we briefly discuss the problem of evaluating the error detection capability of non-exhaustive code over BSC.
Adaptive Multispectral Demosaicking Based on Frequency-Domain Analysis of Spectral Correlation

Color filter array (CFA) interpolation, or three-band demosaicking, is a process of interpolating the missing color samples in each band to reconstruct a full color image. In this paper, we are concerned with the challenging problem of multispectral demosaicking, where each band is significantly undersampled due to the increment in the number of bands. Specifically, we demonstrate a frequency-domain analysis of the subsampled color-difference signal and observe that the conventional assumption of highly correlated spectral bands for estimating undersampled components is not precise. Instead, such a spectral correlation assumption is image dependent and rests on the aliasing interferences among the various color-difference spectra. To address this problem, we propose an adaptive spectral-correlation-based demosaicking (ASCD) algorithm that uses a novel anti-aliasing filter to suppress these interferences, and we then integrate it with an intra-prediction scheme to generate a more accurate prediction for the reconstructed image. Our ASCD is computationally very simple, and exploits the spectral correlation property much more effectively than the existing algorithms. Experimental results conducted on two data sets for multispectral demosaicking and one data set for CFA demosaicking demonstrate that the proposed ASCD outperforms the state-of-the-art algorithms.
Joint visual cryptography and watermarking

In this paper, we discuss how to use the watermarking technique for visual cryptography. Both halftone watermarking and visual cryptography involve a hidden secret image. However, their concepts are different. For visual cryptography, a set of shared binary images is used to protect the content of the hidden image. The hidden image can only be revealed when enough shared images are obtained. For watermarking, the hidden image is usually embedded in a single halftone image while preserving the quality of the watermarked halftone image. In this paper, we propose a joint visual-cryptography and watermarking (JVW) algorithm that has the merits of both visual cryptography and watermarking
Transcoding based robust streaming of compressed video

A variety of techniques have been proposed to enhance the error robustness of the video streaming system. However, most of them improves the error resilience during compression rather than after compression. In this paper, we propose a novel transcoding based scheme called lossless inter frame transcoding (LIFT) scheme to improve the error resilience of existing compressed video stream. In the LIFT scheme, inter coded blocks are selectively transcoded into new kind of blocks called ‘L-block’. At the decoder, the L-block can be transcoded back to the original P-block when the prediction is available and can also be robustly decoded as I-block when the prediction is unavailable. By offline transcoding and online adjusting the ratio of P-blocks and L-blocks, the proposed streaming server achieves error robustness scalability. Experimental results demonstrate the correctness and effectiveness of the proposed method.
An Analytical Model for Synthesis Distortion Estimation in 3D Video

We propose an analytical model to estimate the synthesized view quality in 3D video. The model relates errors in the depth images to the synthesis quality, taking into account texture image characteristics, texture image quality, and the rendering process. Especially, we decompose the synthesis distortion into texture-error induced distortion and depth-error induced distortion. We analyze the depth-error induced distortion using an approach combining frequency and spatial domain techniques. Experiment results with video sequences and coding/rendering tools used in MPEG 3DV activities show that our analytical model can accurately estimate the synthesis noise power. Thus, the model can be used to estimate the rendering quality for different system designs.
Optimal distortion redistribution in block-based image coding using successive convex optimization

In the block-based image coding systems, the pixels at the block boundaries usually have larger distortions than the pixels at other positions. This biased distortion distribution not only generates unpleasant blocking artifact, but potentially decreases the intra-prediction performance and eventually diminishes the coding efficiency. Motivated by this observation, a novel distortion redistribution approach is proposed in this paper which aims to achieve the best rate-distortion performance. Our contributions are summarized in the following aspects: First, the inter-block dependency model (IBDM) is introduced as a quantitative measure of the inter-block dependency. Then, based on the proposed IBDM, the distortion redistribution problem is formulated as a rate minimization problem under a certain distortion constraint. In the end, successive convex optimization method is employed to tackle the optimization problem, and the final optimal distortion can be achieved efficiently. Experimental results demonstrate that compared with the original distortion distribution, significant gain can be obtained with the proposed approach.
Edge-Directed Error Concealment

In this paper we propose an edge-directed error concealment (EDEC) algorithm, to recover lost slices in video sequences encoded by flexible macroblock ordering. First, the strong edges in a corrupted frame are estimated based on the edges in the neighboring frames and the received area of the current frame. Next, the lost regions along these estimated edges are recovered using both spatial and temporal neighboring pixels. Finally, the remaining parts of the lost regions are estimated. Simulation results show that compared to the existing boundary matching algorithm  and the exemplar-based inpainting approach  , the proposed EDEC algorithm can reconstruct the corrupted frame with both a better visual quality and a higher decoder peak signal-to-noise ratio.
A Novel Analytic Quantization-Distortion Model for Hybrid Video Coding

A proper theoretical quantization-distortion model for hybrid video coding is always desirable, since this allows us to explain the behavior of existing codecs and to design better ones. However, due to the existence of motion-compensated prediction, hybrid video coding introduces interframe dependency into the encoded video, which makes its quantization-distortion characteristics difficult to analyze. In this paper, a joint analysis of quantization and motion-compensated prediction is presented. For a complete analysis, we investigate not only the distortion that quantization introduces into video signal, but also its effect on motion-compensated prediction. Based on the joint analysis, a quantization-distortion model of hybrid video coding is proposed. Our extensive experimental results show that the proposed model can estimate the quantization-distortion curve of hybrid video coding with high accuracy. Furthermore, the estimation accuracy remains high for various video sequences and encoder configurations.
A novel radon based Ray-Space interpolation algorithm

Ray-space interpolation is one of the key technologies to make ray-space based FTV (Free Viewpoint Television) system feasible. Since ray-space data is composed of straight lines with different slopes, the problem in ray-space interpolation is to find the slope of the straight lines which tells us the interpolation direction. In this paper, a radon based ray-space interpolation algorithm is proposed. First, feature points of epipolar plane image (EPI) are extracted to form a feature EPI (FEPI). Then, radon transform is applied to FEPI to find the possible interpolation direction. Finally, a new cost function is proposed to improve the smoothness of disparity map and find the optimal interpolation direction. Experimental results show that both the performance and the robustness of the proposed algorithm are much higher than that of the traditional pixel matching based interpolation (PMI) and block matching based interpolation (BMI).
A sequential multiple watermarks embedding technique

In this paper, we propose a novel multiple watermarks embedding scheme. We assume M watermarks have already embedded in the image using M sets of secret key. With the availability of these M sets secret key, another N watermarks can be embedded using the proposed technique while the energies of the watermarks are minimized. And the watermarks embedded later will not interfere with the first M watermarks. Experimental results show watermarked images have good visual quality and the watermarks are robust to JPEG compression and noise attacks.
Joint Decoding of Multiple Video Streams

Due to the storage limit, the original raw video may not exist after compressing it into multiple video bitstreams using different compression parameters. In this paper, we suggest a least square error (LSE) algorithm to jointly decode the multiple video bitstreams, aiming to achieve better quality of the reconstructed video. The experimental results by joint decoding of multiple H.263 streams show that the proposed algorithm can significantly enhance the decoded video quality.
Self-similarity-based image colorization

In this work, we tackle the problem of coloring black-and-white images, which is image colorization. Existing image colorization algorithms can be categorized into two types: scribble-based colorization algorithms and example-based colorization algorithms. Differently, we propose a hybrid scheme that combines the advantages of both categories. Given the grayscale image to be colorized and a few color scribbles (or scattered color labels) as input, the proposed method manages to colorize the grayscale image with high quality. Similar to the mechanisms in example-based colorization methods, our algorithm firstly propagates chrominance information based on the assumption that similar image patches should have similar colors. Therefore colors of some pixels can be transferred from similar patches with known colors. After that, we apply scribble-based colorization algorithm to fully colorize the grayscale image, with different confidences assigned onto the transferred color labels. Experimental results show that, the proposed method effectively utilizes the known chrominance, and provides pleasant colorizations with very few user interventions.
Image colorization via color propagation and rank minimization

Image colorization aims to add colors to grayscale images, which used to be a time-consuming and tedious task that requires lots of human efforts. In this paper, we present a novel colorization method based on color propagation and rank minimization. Given a small portion of chrominance values and a grayscale image, we firstly propagate the known color values to other pixels to be colorized. As the colorized image after color propagation is not accurate, we then define a confidence matrix to measure the propagation fidelity. Finally, pixels that have propagated chrominance values with confidence are colorized by rank minimization, which exploits the redundancy of natural images. Experimental results on real data set show that our proposed method achieves state-of-the-art colorization quality.
Adaptive correlation estimation for general Wyner-Ziv video coding

Wyner-Ziv video coding (WZVC) is a new paradigm for video compression with the prediction frames possibly only available at the decoder. It exploits the statistics between the source frame and the prediction frame at the decoder by utilizing their correlation information. This correlation information is important but also difficult to estimate due to the absolute absence of the prediction frame at the encoder, and the lack of the source frame at the decoder. In this paper, we focus on this issue and derive a coefficient-level adaptive correlation model for general Wyner-Ziv video coding. Based on this model, we propose an online transform-domain adaptive correlation estimation (TRACE) approach, in which the correlation information is progressively learned during the decoding process. In our experiments, the proposed approach outperforms the existing approaches up to 4dB. More importantly, different from existing coefficient-level variance estimation approaches, the proposed on-line TRACE is applicable for not only low complexity WZVC but also other WZVCs such as flexible WZVC as demonstrated in the experiments.
Key frame selection by macroblock type and motion vector analysis

Key frame selection is the process to select the most representative frames of a long video sequence. The paper describes a new algorithm for determining key frames in a video shot. We use macroblock (MB) type and motion vector information of MPEG compressed video to determine the local maxima of visual content change in a shot, i.e. the frames that are most noticeable to the viewer. This algorithm exploits the important cue of the intensity of motion activity and visual content change within a shot from the corresponding MB type and motion vector characteristics. The advantages of this algorithm are the efficient extraction of the required information in the MPEG compressed domain with low complexity VLC decoding and high accuracy. Results show that the proposed algorithm can successfully select the key frames that are sufficient to represent a shot
Image deblocking using convex optimization

Images encoded at low-bit rate may suffer from blocking artifacts, which can dramatically degrade the visual quality. In this paper, a novel approach to image deblocking is presented. Based on the analysis of image coding process and the property of natural images, an objective function and a set of constraint functions are proposed, and image deblocking is formulated as a convex optimization problem which can be easily solved using numerical methods. The feasibility of the convex optimization problem is utilized to detect the true object edges and avoid blurring. Experimental results demonstrates the effectiveness of the proposed approach.
Rate distortion modeling and adaptive rate control scheme for high efficiency video coding (HEVC)

This paper explores a novel rate control scheme for HEVC, based on Pre-analysis Sum of Absolute Transformed Differences (pSATD). The needs for improving the rate control method of HEVC have been observed since the latest method proposed in JCT-VC H213 is still using the classical quadratic model which is implemented in H.264/AVC. The same rate control scheme on different coding structures will definitely lead to a poor performance. The proposed method aims at selecting accurate quantization parameters (QP) for inter frames according to the target bit rate. After encoding video sequences exhaustively, a strong linear relationship between the pSATD and bpp (bit per pixel) is revealed. Thus, a rate control scheme for HEVC with parameters updating adaptively is proposed accordingly. Apart from that, a new rate and distortion model are established using pSATD and Quantization Step Size (QStep). By minimizing the cost function built based on the proposed rate-distortion (R-D) model, the optimization problem can be solved properly. According to the optimal solution, a bit allocation scheme with consideration of pSATD and buffer status is formed. The experimental results illustrate that the proposed method outperforms JCT-VC H213 implemented in HEVC reference software for all test sequences, up to 19% coding gain can be achieved.
Data Hiding with Tree Based Parity Check

In this paper, we propose a novel algorithm namely tree based parity check (TBPC) that can be applied to most of the existing data hiding algorithms to achieve improvement in visual quality. In data hiding process, distortion is created when the original image is modified. Most existing data hiding algorithms try to minimize the visual artifacts introduced by the modifications. The proposed algorithm tries to reduce the probability of modifying the original host image. Theoretical analysis and experimental results are given in this paper. Both measures suggest that an improvement in visual quality is achieved in the watermarked image.
Universal Binary Semidefinite Relaxation for ML Signal Detection

Semidefinite relaxation (SDR) provides a computationally efficient polynomial-time approximation of the maximum likelihood detector. However, most of the existing works mainly focus on particular signal constellations. In this paper, we propose a universal binary semidefinite relaxation scheme that can handle arbitrary signal constellations in polynomial time. The proposed scheme first binarizes the original signal space to a linearly constrained binary space, and then solves the detection problem through SDR.colorblack{{} A specialized dual barrier method is provided to solve the SDR more efficiently. In addition, we propose to apply on-the-fly decision feedback to further reduce the computational complexity and improve the detection performance. The} proposed binary SDR, together with on-the-fly decision feedback scheme, can provide comparable or better solutions compared to existing SDR methods specialized to specific constellations such as 16-QAM and 8-PSK in terms of computational complexity and symbol error rate. Furthermore, the proposed scheme is universal and can solve any other constellations such as 12-QAM, 32-QAM, or M-PSK.