nils-herrmann/clean_scirepeval huggingface dataset:
Image characteristic oriented tone mapping for high dynamic range images

This paper presents a novel and efficient tone mapping algorithm for converting high dynamic range (HDR) images back to low dynamic range (LDR) images for displaying purpose because of the limited contrast ratio of common displays and printers. As the ratio between the maximum and minimum values of common HDR images is always very large and also the population usually deflects to one side, for convenient processing, most researchers first take the logarithm on the luminance layer or use another adaptive mapping to shorter the range of the distribution in their tone mapping methods. However, these mappings have already distorted the original imagespsila characteristics. In this paper, there is no such adverse mapping applied on the luminance layer in the proposed tone mapping algorithm. The paper does produce a tone reproduction curve to convert HDR images to LDR images. Adaptive techniques are also manipulated to provide better visual quality. The whole process is automatic and no parameter is required for manual input. The result will be a superior visual quality tone mapped LDR image with original HDR imagepsilas characteristics.
Reconfigurable hardware-friendly CU-group based merge/skip mode for high efficient video coding

Merge/skip mode is one of the most important inter prediction tools adopted in the High Efficiency Video Coding (HEVC) standard which is the state-of-the-art video coding standard. It is very efficient in reducing the side information for the blocks within the same object. However, it is difficult for parallel encoding and decoding due to the data dependency problem between neighboring prediction units (PU). Furthermore, different shapes and positions of PUs would result in different definition of the merge/skip candidate list (MCL), which would lead to potentially extra hardware cost and is not easy to be efficiently implemented by the hardware. To deal with this problem, two reconfigurable hardware-friendly MCL construction schemes are proposed in this paper. The first scheme which is called unified MCL (UMCL) uses one candidate list for all PUs inside the motion estimation region (MER), which is regarded as the basic parallel processing unit for the hardware realization. The second scheme which is named boundary MCL (BMCL) allows different candidate lists for the PUs on the boundary of MER. Both of the two schemes can have flexible parallel degree based on the requirement specification. Experimental results show that UMCL reduces the hardware complexity significantly with little coding performance degradation and BMCL achieves significant coding gain while maintaining the hardware complexity.
Joint Denoising and demosaicking of noisy CFA images based on inter-color correlation

Most digital cameras use a single sensor coupled with a Color Filter Array (CFA) to capture images, and apply demosaicking to interpolate the full color images. In reality, the CFA image is noisy, which causes problems in the demosaicking process. This paper proposes a Joint Denoising and Demo-saicking based on inter-Color correlation (JDDC) scheme. We propose a new framework that linearly combines an extracted luminance image and a low-passed RGB images to get a full color image. Given the noise in the extracted luminance image and the low-passed RGB images are non-stationary and partially correlated, we modify the classical Non-Local Means (NLM) filter to denoise the extracted luminance image and the low-passed RGB images before the combination. Experimental results verify the effectiveness of the proposed scheme both objectively and subjectively.
A novel predictive global motion estimation for video coding

Global motion compensation improves the coding efficiency by reducing the transmitted motion vectors and reconstructs a more accurate prediction image for texture coding. Predictors are widely in local motion estimation and it is well proven that the use of predictors can speed up the local motion estimation process. In this paper, we propose a novel predictive global motion estimation algorithm, which combines the prediction idea and global motion estimation. The proposed algorithm first examines sum absolute difference of a set of candidate predictors and uses the predictor: with minimum SAD as the initial search center of N-dimensional diamond search to find the final global motion parameter. Although the PSNR of proposed algorithm is slightly lower than MPEG-4 VM GME, the speed of the proposed algorithm is significantly faster.
Image de-quantization via spatially varying sparsity prior

We address the problem of image de-quantization, which is also known as bit-depth expansion if the reconstructed 2D signal is re-quantized into higher bit-precision. In this paper, a novel image de-quantization method based on convex optimization theory is proposed, which exploits the spatially varying characteristics of image surface. We test our method on image bit-depth expansion problems, and the experimental results show that proposed method can achieve superior PSNR and SSIM performance.
Color video denoising based on adaptive color space conversion

Denoising is one of the most common and important task in video processing systems and abundant efforts have been made on video denoising nowadays. Multihypothesis motion compensated filter (MHMCF) is an effective video denoising method, which combines multiple hypotheses obtained from motion estimation through a number of reference frames by weighted average to suppress noise. However, MHMCF only considers denoising of grayscale video signal. In this paper, we apply MHMCF to color video denoising, where the RGB video is first transformed to the luminance-color difference space before denoising. Instead of using traditional YCbCr color conversion, we propose a novel color conversion matrix which is adaptive to the noise variance in R, G, B channels. Simulation results demonstrate that our proposed color space conversion method can successfully improve the denoising performance for color video.
Motion vector coding based on predictor selection and boundary-matching estimation

In most of recent video coding standards based on block-based hybrid coding scheme, especially in the state-of-the-art H.264/AVC standard, motion vector information occupies a considerable portion of the whole compressed bitstream. There-fore, the efficient coding of motion vectors has become an essential objective to further reduce the bitrate. In this paper, we propose a novel motion vector coding method based on predictor selection and estimation. First, the optimal motion vector predictor (MVP) is chosen from a predefined predictor candidate set consisting of both spatial predictors and temporal predictors to minimize the number of bits used for encoding motion vector difference (MVD). Then, in order to avoid sending extra bits for informing the decoder which candidate is selected by the encoder, boundary-matching (BM) estimation is applied at the decoder side to find out the optimal predictor. The basic principle of BM estimation is to preserve the spatial continuity of boundaries between currently reconstructed block and its neighbors. Simulation results show that compared to the original H.264/AVC codec, the proposed scheme improves coding efficiency for various video sequences.
Soft-Decision Color Demosaicking with Direction Vector Selection

We propose a soft-decision color demosaicking algorithm with direction vector selection which effectively minimizing color artifacts. Since our interpolation uses soft decision and decision making bases on direction vectors which consists of three primary colors together with same direction, it not only maintains the direction consistency, but also significantly reduces color artifacts by largely avoiding interpolation across the edge. Experimental results show that our proposed algorithm outperforms state-of-art methods and the visual quality of reconstructed images is also obviously improved.
Subpixel-based down-sampling via Min-Max Directional Error

Subpixel-based down-sampling is a method that can potentially improve the apparent resolution of a down-scaled image by controlling individual subpixels rather than pixels. However, the increased luminance resolution often comes at the expense of chrominance distortion. In this paper, we formulate the subpixel-based down-sampling as a Min-Max problem (MinMax Directional Error) which we call MMDE. Unfortunately, the solution of MMDE is computational intensive, especially for large images. We thus relax the MMDE by determining the maximum error based on HVS, which largely reduces the number of constraints. We call such relaxation as MMDE-VR (Visual Relaxation). Simulation results illustrate that MMDE-VR can effectively reduce visible color fringing artifacts while still maintaining sharpness.
Rate control using probability of non-zero quantized coefficients [video coding]

Most existing rate control schemes in the literature calculate quantization parameters of the macro-blocks (MB) based on the standard deviation of the residue before quantization. The probability of non-zero coefficients after quantization is a new factor that has an interesting property. The rate tends to be linear with this probability. In this work, we design the rate control based on this special factor with its linear property. This work investigates the relationships of bit rate and distortion with MB quantization step size and its interesting probability. We establish linear rate and linear distortion models and propose a rate control scheme based on these models. The proposed algorithm has a low quantization overhead, low computation complexity and is independent of the distortion factors, compared with TMN8 rate control. The experimental results suggest that our scheme can achieve PSNR gain over TMN8.
Subpixel-Based Image Down-Sampling With Min-Max Directional Error for Stripe Display

Subpixel-based down-sampling is a method that can potentially improve the apparent resolution of a down-scaled image by controlling individual subpixels rather than pixels. However, the increased luminance resolution often comes at the price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. In this paper, we formulate the subpixel-based down-sampling as a Min-Max problem (Min-Max Directional Error) which we call MMDE. Unfortunately, the solution of MMDE is computational intensive, especially for large images. We thus relax the MMDE by approximating the maximization operation based on HVS, which we call MMDE with Visual Relaxation (MMDE-VR). Although MMDE-VR can reduce the computational complexity of MMDE largely, it is still not acceptable for real-time implementation. We make a further simplifying assumption based on the edge relationship of neighboring pixels, which we call MMDE with Edge Relaxation (MMDE-ER). Simulation results show that MMDE-VR and MMDE-ER can give significant sharper images compared with the conventional pixel-based down-sampling methods, with little color fringing artifacts.
Bag of textons for image segmentation via soft clustering and convex shift

We propose an unsupervised image segmentation method based on texton similarity and mode seeking. The input image is first convolved with a filter-bank, followed by soft clustering on its filter response to generate textons. The input image is then superpixelized where each belonging pixel is regarded as a voter and a soft voting histogram is constructed for each superpixel by averaging its voters' posterior texton probabilities. We further propose a modified mode seeking method - called convex shift - to group superpixels and generate segments. The distribution of superpixel histograms is modeled nonparametrically in the histogram space, using Kullback-Leibler divergence (K-L divergence) and kernel density estimation. We show that each kernel shift step can be formulated as a convex optimization problem with linear constraints. Experiment on image segmentation shows that convex shift performs mode seeking effectively on an enforced histogram structure, grouping visually similar superpixels. With the incorporation of texton and soft voting, our method generates reasonably good segmentation results on natural images with relatively complex contents, showing significant superiority over traditional mode seeking based segmentation methods, while outperforming or being comparable to state of the art methods.
New chroma intra prediction modes based on linear model for HEVC

RGB-to-YUV conversion reduces the inter-channel redundancy in video compression. However, inter-channel correlation is still observed locally. In HEVC HM4.0, LM mode uses a linear model to predict chroma from luma as a chroma intra prediction mode. The linearity is derived from the reconstructed causal pixels. Specifically, for a NxN chroma block, the above N neighbors and the left N neighbors are used for derivation. Considering the variety of the content inside a block, the linearity derived in LM mode does not always match the linearity of the current block. In this paper, we propose two new modes LML and LMA for chroma intra prediction. Those two modes are quite similar to LM mode, except that they use different neighbors for linearity derivation. By adding the proposed modes, about an average of 0.2%, 5.9%, 6.7% BD-rate reduction is achieved under all intra configuration for Y, Cb and Cr components respectively.
A comprehensive study on digital image matting

Natural image matting refers to the problem of extracting the region of interest such as foreground object from an image based on user inputs like scribbles or trimap. Matting is an ill-posed problem inherently since we need to output three images out of only one input image. After a comprehensive survey and analysis of the existing matting literature, we observe that there are three key components in better estimating the alpha values, that is, the design of matting laplacian matrix, the definition of neighborhood and the choices of feature space. Based on this observation, we introduce a unified framework for digital image matting, which provides the possibility of obtaining a better understanding and direction of further improvement for image matting problem. The experimental results tested on different matting algorithms further prove the feasibility of our proposed framework.
Error Concealment for Frame Losses in MDC

Multiple description coding (MDC) is an effective error resilience (ER) technique for video coding. In case of frame loss, error concealment (EC) techniques can be used in MDC to reconstruct the lost frame, with error, from which subsequent frames can be decoded directly. With such direct decoding, the subsequent decoded frames will gradually recover from the frame loss, though slowly. In this paper we propose a novel algorithm using multihypothesis error concealment (MHC) to improve the error recovery rate of any EC in the temporal subsampling MDC. In MHC, the simultaneous temporal-interpolated frame is used as an additional hypothesis to improve the reconstructed video quality after the lost frame. Both subjective and objective results show that MHC can achieve significantly better video quality than direct decoding.
A novel content-adaptive video denoising filter

We propose a simple non-linear content-adaptive filter that is efficient in removing noise from a video. The proposed filter is called spatiotemporal varying filter (STVF) and is able to produce optimal results in the sense that it minimizes the weighted least square error. STVF combines the advantages of conventional denoising filters that enable it to decrease the noise variance in smooth areas but at the same time retains the sharpness of edges in object boundaries. Simulation results show that STVF outperforms the conventional denoising methods like low-pass filtering, median filtering and Wiener filtering.
A parallel deblocking filter based on H.264/AVC video coding standard

The deblocking filter in H.264/AVC is one of the most time consuming part of video decoder as its high content adaptation and data dependency lead to lots of computation. In this paper, we propose a novel parallel deblocking filter design based on the H.264/AVC video coding standard, taking the advantage that the data dependency of the deblocking filter are “periodic” in one dimension. Our proposed architecture successfully reduces the dependency between horizontal and vertical filters and utilizes the “periodic” property to achieve pixel-level parallelism. Algorithm analysis and experiment results on JM and GPU show that the proposed deblocking filter keeps as good a coding efficiency as that in H.264/AVC, and its high parallelism is suitable and promising in multi-core/multi-thread computing.
Cultural Data Sculpting: Omnispatial Visualization for Cultural Datasets

This paper presents four research projects currently underway to develop new omni spatial visualization strategies for the collaborative interrogation of large-scale heterogeneous cultural datasets using the worlds' first 360-degree stereoscopic visualization environment (Advanced Visualization and Interaction Environment - AVIE). The AVIE system enables visualization modalities through full body immersion, stereoscopy, spatialized sound and camera-based tracking. The research integrates work by a group of international investigators in virtual environment design, immersive interactivity, information visualization, museology, visual analytics and computational linguistics. The work is being implemented at the newly established research facility, City University's Applied Laboratory for Interactive Visualization and Embodiment -- ALIVE) in association with partners Museum Victoria (Melbourne), iCinema Centre, UNSW (Sydney), ZKM Centre for Art and Media (Karlsruhe), UC Berkeley (USA), UC Merced (USA) and and Europeana (in association with Israel Museum of Jerusalem). The applications are intended for museum visitors and for humanities researchers. They are: (1) Data Sculpture Museum, (2) Inside Europeana, (2) Rhizome of the Western Han, (4) Blue Dots AVIE (Tripitaka Koreana).
Color bleeding reduction in image and video compression

Color bleeding is an annoying chromatic color artifact in DCT-coded technology. We propose an efficient postprocessing technique in YCbCr color space, which is commonly used in image and video compression, based on adaptive filtering specifically to reduce color bleeding artifacts while preserving the edge sharpness of the reconstructed images. Simulation results show that the method effectively improves image quality both objectively and subjectively. The PSNR in chroma channels gains a lot and the visual quality is improved effectively.
Novel fast motion estimation for frame rate/structure conversion

Different multimedia applications and transmission channels require different resolution, frame rates/structures and bitrate; there is often a need to transcode the stored compressed video to suit the needs of these various applications. This paper is concerned about fast motion estimation for frame rate/structure conversion. In this paper, we proposed several novel algorithms that exploit the correlation of the motion vectors in the original video and those in the transcoded video. We achieve a much higher quality than existing fast search algorithms with much lower complexity.
Novel 2-D MMSE subpixel-based image down-sampling for matrix displays

Subpixel-based down-sampling is a method that can potentially improve the apparent resolution of a down-scaled image by controlling individual subpixels rather than pixels. However, the increased luminance resolution often comes at the price of chrominance distortion. In this paper, we propose a new subpixel-based down-sampling scheme for which we design a two-dimensional image reconstruction model. Then, we formulate subpixel-based down-sampling as a MMSE problem and derive the optimal solution called MMSESD. To compare the performance of subpixel-based down-sampling methods, we propose novel objective measures for the apparent luminance resolution and chrominance distortion. Simulation results show that MMSE-SD can give sharper images compared with the conventional down-sampling methods, with little color fringing artifacts.
Exact Symbol Error Rate for Variable Length Codes Over Binary Symmetric Channel

In this paper, we analyze the error recovery performance of variable length codes (VLCs) transmitted over binary symmetric channel (BSC). Simple expressions for the exact mean symbol error rate (MSER) and the exact variance of symbol error rate (VSER) for any crossover probability pe are presented. We also prove that the mean error propagation length (MEPE) derived for single bit inversion error case is a scaled value of MSER when pe tends to zero. Comparisons with simulations demonstrate the accuracy of the MSER and VSER expressions.
Simultaneous RD-optimized rate control and video de-noising

In this paper, we propose a simultaneous rate control and video de-noising algorithm based on rate distortion optimization. According to our previous works, video de-noising can be performed by using rate distortion optimization with a lower bound quantization parameter (QP) constraint, where the lower bound QP is determined by the noise variance. Then, we find that the macroblock level rate control method in H.264 can be seen as an approximate solution of a rate distortion optimization problem with a specified rate distortion function. Based on these two studies, we integrate the video de-noising problem and rate control problem to a rate distortion optimization problem. We show the convexity of the problem and derive the optimal solution. To reduce the complexity, we propose to use a suboptimal solution based on simply thresholding. Some experiments are conducted to demonstrate the efficiency and effectiveness of the proposed method.
An analytical study of subpixel-based image down-sampling patterns in frequency domain

Subpixel-based image down-sampling is a class of methods that can provide improved apparent resolution of the down-scaled image compared to the pixel-based methods. The frequency characteristics of all possible subpixel-based down-sampling patterns for RGB vertical stripes are analytically studied in this paper. Our proposed algorithm reveals that there are merely seven equivalent energy distributions in the luminance frequency spectrum. To achieve higher luminance resolution, we then calculate and choose the optimal down-sampling pattern with anti-aliasing low-pass filter designed for it so as to maximize the energy of the luminance component within the cut-off shape. Experimental results show that the proposed method provides sharper images compared to the state-of-art subpixel-based methods, with little color distortion.
Improved sample adaptive offset for HEVC

High-Efficiency Video Coding (HEVC) is the newest video coding standard which can significantly reduce the bit rate by 50% compared with existing standards. One new efficient tool is sample adaptive offset (SAO), which classifies reconstructed samples into different categories, and reduces the distortion by adding an offset to samples of each category. Two SAO types are adopted in HEVC: edge offset (EO) and band offset (BO). Four 1-D directional edge patterns are used in edge offset type, and only one is selected for each CTB. However, single directional pattern cannot remove artifacts effectively for the CTBs, which contain edges in different directions. Therefore, we analyze the performance of each edge pattern applied on this kind of CTB, and propose to take advantage of existing edge classes and combine some of the them as a new edge offset class, which can adapt to multiple edge directions. All the combinations are tested, and the results show that for Low Delay P condition, they can achieve 0.2% to 0.5% bit rate reduction.