nils-herrmann/clean_scirepeval huggingface dataset:
Local saliency detection based fast mode decision for HEVC intra coding

The High Efficiency Video Coding (HEVC) is the next generation video coding standard beyond H.264/AVC. Compared with only up to 9 modes for intra prediction in H.264/AVC, HEVC provides 35 intra prediction modes (IPM) to improve coding efficiency, which inevitably poses a huge complexity burden to the encoder. To speed up the HEVC encoder, a novel fast mode decision (FMD) algorithm for HEVC intra prediction is proposed. In the proposed algorithm, we analyzed the costs generated by rough mode decision (RMD), which has already been incorporated in the HM software. We found that the RMD costs listed by mode number generally follow the same trend with the rate-distortion optimization (RDO) costs. Further, the local salient modes, whose RMD costs have a significant drop compared with adjacent modes, tend to be promising competitors for the optimal mode. Based on these observations, we further reduced the number of the candidates for the RDO process. Experimental results show that our proposed algorithm achieves averagely 19.0% (up to 33.6%) encoding time saving whilst causing negligible RD performance loss (0.4% BD-Rate increase on average) compared with HM 7.0 anchor.
On improving the robustness of compressed video by Slepian-Wolf based lossless transcoding

A variety of techniques have been proposed to enhance the error robustness of the video streaming system. However, most of them improve the error resilience during compression rather than after compression. In this paper, we propose a novel transcoding based scheme called Slepian-Wolf based inter frame transcoding (SWIFT) to improve the error resilience of existing compressed video stream. In the SWIFT scheme, inter coded blocks are selectively transcoded into new kind of blocks called ‘X-block’. At the decoder, the X-block can be transcoded back to the original P-block when there is no error in the prediction, and can also be robustly decoded as I-block when there are errors in the prediction. In the experiments, the proposed SWIFT scheme does not introduce transcoding distortion as expected, and always improves the robustness of the compressed video at all packet loss rate. Compared with the H.264 based transcoder, SWIFT achieves better RD performance and error resilience performance.
Adaptive search range algorithm based on Cauchy distribution

In video coding standard, motion estimation (ME) always plays an important role in reducing temporal redundancies at the expense of higher computational complexity. Many fast ME algorithms have been proposed to reduce the coding complexity. Some papers focus on applying specific search patterns to reduce the search points within a fixed search range (SR). But there are only a few of them trying to reduce the size of SR. In this paper, an adaptive SR algorithm is presented. Cauchy distribution is used to model the SR for one frame and the information of motion vector differences in the neighboring blocks is used to adjust the SR for a particular block. Experimental results show that the proposed algorithm can reduce the size of SR significantly with negligible quality degradation.
High Dynamic Range image capturing by Spatial Varying Exposed Color Filter Array with specific demosaicking algorithm

High Dynamic Range (HDR) imaging is a future trend for digital imaging. With excessive spatial resolution in digital cameras nowadays, plenty of spaces remain in the dynamic domain to enhance the image quality. In order to produce HDR image using conventional devices, multiple captures with different exposure settings are performed and combined. However, multiple exposure systems requires static photo scene. In this paper, a Spatial Varying Exposure (SVE) system is proposed. By altering the exposure settings in spatial domain, it is possible to capture HDR image from instantaneous scene by trading-off spatial resolution. Moreover, a specific demosaicking algorithm is designed to conceal the color pixels assigned to different exposure fields.
On improving the iterative watermark embedding technique for JPEG-to-JPEG watermarking

We previously proposed an iterative technique called iterative watermark embedding (IWE) for watermark embedding in JPEG compressed domains. The proposed technique embeds the watermark successfully when the quality factor of the JPEG image is high, but when the quality factor is low, not all the bits of the watermark can be embedded. This is due to uneven distribution of capacities in different regions of the image. Moreover, the computation complexity of the iterative loop is high. In this paper we improve the technique using shuffling to equalize the capacities of different regions. Also, the iterative loop is replaced by a direct approach to reduce the computational complexity. We call this direct approach Direct JPEG Watermarking Embedding (DJWE). A Human Visual System (HVS) model is used to prioritize the DCT coefficients to achieve good visual quality. Experimental results show that the watermarks are robust against Gaussian noise attack and JPEG transcoding attacks.
A fast NL-Means method in image denoising based on the similarity of spatially sampled pixels

As one of the best image denoising methods, the Non-Local Means(NL-Means)algorithm[5] proposed by Buades et al. generates state-of-the-art performance. However, due to the high computational complexity, it is difficult to be directly used in practical applications. In this paper, a novel fast algorithm based on the similarity of spatially sampled pixels is introduced. Compared with other fast approaches, the result has shown that our method always uses the shortest time. Meanwhile, it keeps a similar or even better visual results. A maximum of 0.9dB improvement can be attained in comparison with the original method when the noise variance is small.
Scalable Compression of Stream Cipher Encrypted Images Through Context-Adaptive Sampling

This paper proposes a novel scalable compression method for stream cipher encrypted images, where stream cipher is used in the standard format. The bit stream in the base layer is produced by coding a series of nonoverlapping patches of the uniformly down-sampled version of the encrypted image. An off-line learning approach can be exploited to model the reconstruction error from pixel samples of the original image patch, based on the intrinsic relationship between the local complexity and the length of the compressed bit stream. This error model leads to a greedy strategy of adaptively selecting pixels to be coded in the enhancement layer. At the decoder side, an iterative, multiscale technique is developed to reconstruct the image from all the available pixel samples. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-arts in terms of both rate-distortion performance and visual quality of the reconstructed images at low and medium rate regions.
Bidirectionally decodable Wyner-Ziv video coding

Inter frame prediction technique significantly improves the compression efficiency in the hybrid video coding schemes. However, this technique causes the decoding dependency of each inter frame on all of its reference frames. This dependency complicates the reverse play operation which is the most common video cassette recording (VCR) functions. This dependency also causes error propagation when the video is transmitted over error prone channel. In this paper, we propose a novel bidirectionally decodable Wyner-Ziv video coding scheme which relaxes this inter frame dependency. The proposed bidirectionally decodable Wyner-Ziv frame can be decoded by using whether forward prediction or backward prediction as side information at the decoder, i.e. the proposed stream supports forward decoding and backward decoding simultaneously. Compared with the other schemes which support reverse playback, our scheme requires much lower bandwidth and smaller storage space. In error resilient test, our scheme outperforms H.264 up to 4dB at same bitrate. Our proposed frames also support video splicing and stream switching at arbitrary time point like I-frames.
HEVC-based adaptive quantization for screen content by detecting low contrast edge regions

High-Efficiency Video Coding (HEVC) is the newest video coding standard which can significantly reduce the bit rate by 50% compared with existing standards. The key features and new tools in HEVC are designed for natural video sequences captured by a real camera. Different from natural videos, screen content contain much more edges in text and icon regions. The current video coding standards may blur or even remove low contrast edges, which are very important in screen content for human eyes to recognize the character and the icon. Therefore, this paper proposes an effective modification on HEVC to preserve the low contrast edges in screen content. First, discrete laplacian filter is adopted for edge detection, and then we adaptively adjust QPs for low contrast edge regions, which can be detected based on our designed measurement for edge contrast. Experimental results show that nearly all the regions containing low contrast edges can be detected, and the adjustment of QPs for these regions can greatly protect the edges with no RD performance reduction.
Mesh Segmentation with Concavity-Aware Fields

This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information. The method locates concave creases and seams using a set of concavity-sensitive scalar fields. These fields are computed by solving a Laplacian system with a novel concavity-sensitive weighting scheme. Isolines sampled from the concavity-aware fields naturally gather at concave seams, serving as good cutting boundary candidates. In addition, the fields provide sufficient information allowing efficient evaluation of the candidate cuts. We perform a summarization of all field gradient magnitudes to define a score for each isoline and employ a score-based greedy algorithm to select the best cuts. Extensive experiments and quantitative analysis have shown that the quality of our segmentations are better than or comparable with existing state-of-the-art more complex approaches.
Image registration method based on local high order approach

Image registration based on gradient and least square optimization technique is one of the most edge-cutting registration algorithms. Such method, especially useful for sub-pixel motion, searches for the best motion in an iterative way. This paper solves the same motion registration problem following this direction. And the well-known Gauss-Newton method (GNM) is employed here as the optimization tool. To achieve a speed-up and reduction of the arithmetic calculation, a simplified high order approach(SHoA) used to calculate several parameters for GNM is introduced. Detailed complexity analysis and performance comparison are presented showing that such an approach is a better trade-off between registration error and the number of math operations.
Natural image matting via adaptive local and nonlocal sample clustering

Digital image matting is the determination of foreground color, background color, and an opacity value of each pixel for an input image. Inherently, matting is a highly ill-posed and under-constrained problem. Thus, some assumptions need to be made to resolve it. Inspired by closed-form matting and color clustering matting, in this work, we first develop an adaptive sample clustering criterion to automatically assign either local or nonlocal neighborhood to each pixel. After that, in order to enhance matting accuracy, we improve the nonlocal clustering performance by introducing a new feature selection parameter to choose preferred feature space for different images in a fully automatic way. And finally we solve the problem using a closed form solution. Experimental results show that our algorithm achieves equal or even better performance among many state-of-the-art matting techniques.
Graph-based joint denoising and super-resolution of generalized piecewise smooth images

Images are often decoded with noise at receiver due to capturing errors and/or signal quantization during compression. Further, it is often necessary to display a decoded image at a higher resolution than the captured one, given available high-resolution (HR) display or a need to zoom-in for detailed examination. In this paper, we address the problems of image denoising and super-resolution (SR) jointly in one unified graph-based framework, focusing on a special class of signals called generalized piecewise smooth (GPWS) images. GPWS images are composed mostly of smooth regions connected by transition regions, and represent an important subclass of images, including cartoon, sub-regions of video frames with captions, graphics images in video games, etc. Like our previous work on piecewise smooth (PWS) images, GPWS images also imply simple-enough graph representations in the pixel domain, so that suitable graph-based filtering techniques can be readily applied. Specifically, leveraging on previous work on graph spectral analysis, for a given pixel block in low-resolution (LR) we first use the second eigenvector of a computed graph Laplacian matrix to identify a hard boundary, and then use the third eigenvector to identify two piecewise smooth regions and a transition region that separates them. The LR hard boundary is then super-resolved into HR via a procedure based on local self-similarity, while graph weights of the LR transition region is mapped to those of the HR transition region via polynomial fitting. Using the computed HR boundary and weights in the transition region, we construct a suitable HR graph corresponding to the LR counterpart, and perform joint denoising / SR using a graph smoothness prior. Experimental results show that our proposed algorithm outperforms two representative separable denoising / SR schemes in both subjective and objective quality.
Adaptive Chosen-Ciphertext Attack on Secure Arithmetic Coding

The paper ldquosecure arithmetic codingrdquo (in 2272, May 2007) presented a novel encryption scheme called the secure arithmetic coding (SAC) based on the interval splitting arithmetic coding (ISAC) and a series of permutations. In the current work, we study the security of the SAC under an adaptive chosen-ciphertext attack. It is shown that the key vectors used in the codeword permutation step can be recovered with complexity O(N), where N is the symbol sequence length. After getting these key vectors, we can remove the codeword permutation step, and the resulting system has already been shown to be insecure in the original paper. This implies that the SAC is not suitable for the applications where the attacker can have access to the decoder. In addition, we discuss a method to jointly enhance the security and the performance of the SAC.
Cryptanalysis of secure arithmetic coding

This work investigates the security issues of the recently proposed secure arithmetic coding (AC), which is an encryption scheme incorporating the interval splitting AC with a series of symbol and codeword permutations. We propose a chosen-ciphertext attack which is capable of recovering the key vectors for codeword permutations with complexity O(N), where N is the symbol sequence length. After getting the key vectors for codeword permutations, we can remove the codeword permutation module, and the resulting system has already been shown to be insecure in the original paper.
An advanced zonal block based algorithm for motion estimation

Efficient motion estimation is very important for compressing video in standards like MPEG1/2/4 and ITU-T H.261/263. In this paper a new algorithm is presented which can outperform most of the traditional fast motion estimation algorithms in both speed and quality. In addition, in some cases this algorithm can achieve even better visual quality, than the “optimal” but computational intensive “full search” algorithm.
Optimal contrast enhancement for tone-mapped low dynamic range images based on high dynamic range images

Tone-mapping refers to methods for rendering high dynamic range (HDR) images on low dynamic range (LDR) displays. However, strong tonal compression in tone mapping process often causes those tone-mapped LDR images to have much less local contrast compared with the original HDR images. In this paper, we propose a novel contrast enhancement algorithm to bring the local contrast of the tonemapped images closer to that of the HDR images and formulate it as an optimization problem. Simulation results suggest that the proposed method can greatly enhance the visual quality of the tone-mapped images by bringing out details in the extreme bright and dark regions.
Halftone Image Data Hiding with Block-Overlapping Parity Check

In this paper, we propose an innovative algorithm, namely block-overlapping parity check (BOPC), that can be applied to the existing halftone image data hiding algorithms data hiding smart pair toggling (DHSPT) to achieve improvement in visual quality. The proposed algorithm utilizes the properties of block-overlapping parity check to reduce the number of pair toggling required in DHSPT. Our experiments suggest that the proposed algorithm reduces the number of pixel pair toggling significantly and has a better performance in terms of modified peak-signal-to-noise ratio (MPSNR), especially when the watermark payload is high.
Fast fractal encoding in frequency domain

Fractal image compression applies the self-similarity property of an image. Much research has been done to study the properties of fractal coding in the image domain. In this paper, however, we try to explore the features of fractal coding in the frequency domain. We firstly overview the properties of fractal coding in the image domain, then we derive the corresponding formula of scaling factor and offset of affine transform in the DCT domain. Applying the energy compaction property of the DCT, we propose a fast fractal encoding algorithm by using only a small number of low frequency DCT coefficients in measuring the similarity between range block and domain block. We further propose a possible fast hybrid fractal encoding algorithm which combines existing fast search methods, statistical normalization and frequency domain comparison.
Simplified generalized residual prediction in scalable extension of HEVC

Scalable video coding (SVC), which is an extension of H.264/AVC video coding standard, was introduced to provide scalability in different dimensions for adaptation to heterogeneous network and terminals. After the finalization of the new video coding standard called High Efficiency Video Coding (HEVC), the effort of the standardization committee has been redirected to the investigation of the scalable extension of HEVC. In addition to the basic inter-layer texture prediction mechanism, several other coding tools were proposed for coding performance improvement. Among those coding tools, the one called generalized residual prediction (GRP) scheme achieves most significant coding gain while the consumption of computational power is also huge. In this paper, the GRP mechanism is formulated and analyzed in detail. In addition, the combination of GRP mechanism with merge mode in HEVC is proposed for simplification of the existing GRP mechanism. Results show a better trade-off could be achieved by greatly reducing computational complexity while maintaining most of the coding gain.
LMMSE frequency merging for demosaicking

For raw images captured by most digital cameras, every pixel has only on color in R, G and B. Kinds demosaicking algorithms are proposed for interpolating the missing two colors. In this article, the relationships inter and intra color channels are analyzed, and basing on the features, we propose a method to divide raw images into sub images and merge them in frequency domain with linear combination. Optimal weights are calculated with estimation values and raw values based on minimum mean square error criteria. Experiments results with different estimations are presented and discussed.
Interactive multiview video scheduling through bargaining

This paper proposes a multiview video scheduling scheme through asymmetric Nash bargaining for a tradeoff between user satisfaction and view fairness. The interactions of view streams queueing at the scheduler are modeled as a bargaining problem, where packets of different views share the transmission opportunities via negotiation. A utility function based upon the viewing angle is defined to indicate whether a view is desired by a user, then a network utility maximization problem is solved by the Nash bargaining solution (NBS). The impacts of the bargaining powers on both network performance and view fairness are analytically investigated. Simulation results are provided to compare the performances of three scheduling schemes including fair scheduling, symmetric bargaining, and proportional scheduling, which can be achieved by adjusting the bargaining powers.
Shape-oriented segmentation with graph matching corroboration for silhouette tracking

This paper addresses the problem of advanced silhouette tracking with no prior information, and proposes shape-oriented segmentation together with graph matching corroboration. In terms of unified energy minimization, the shape-oriented graph cut in segmentation exploits the shape information by penalizing the feature points in alignment with shape-oriented map of adjacent frames. While reducing the temporal inconsistencies and improve the accuracy of segmentation, the energy model of graph matching is further designed to compensate the validity of segmentation. To be concrete, it is involved with structural matching cost and unmatched penalty cost to deal with occlusion during tracking. The effectiveness of the proposed scheme is shown with experiments on challenging real-world image sequences.
An error spreading scheme for video streaming over the Internet

A novel error control method, called bit-plane based error spreading (BES), is introduced. It can spread the heavy error caused by the bursty packet losses. In order to spread out bursty errors, the proposed method interleaves the video packets of one VOP's FGS enhancement layer bit-planes with those of other VOP's enhancement layer bit-planes. Consequently, bursty packet losses of FGS enhancement layer are separated in different VOP's, and image quality can be smoothed. In addition, in order to enhance the packet loss resilience capability of the FGS decoder, we made some modification to the typical FGS decoder, and the experimental results shows that this modification is feasible and effective.
Transmission distortion modeling for wireless video communication

In video transmission over wireless networks, the channel is error-prone and the compressed video data is highly sensitive to errors. The transmission errors causes decoding failure and distort the pictures. This type of picture distortion is called transmission distortion. In this work, we develop a predictive approach for transmission modeling so that the encoder is able to estimate the average transmission distortion of the decoded video data. Our simulation results demonstrate that the proposed model is robust and accurate. Using this type of predictive transmission distortion model, the encoder is able to intelligently allocate its resources, such as bits and energy, to maximize the decoded video quality at receiver side