nils-herrmann/clean_scirepeval huggingface dataset:
Region-based backlight compensation algorithm for images and videos

An algorithm for compensating the effects of backlight in images and videos is proposed in this work. We first determine the region of interest (ROI) to compensate mainly using a saliency map, which is based on darkness, skin color, color and texture prominency features. We then compute the compensating offset value for each pixel. Initial offset values are derived to improve the brightness and the contrast of the ROI, and also to provide temporally consistent output frames in case of the video compensation. Finally, we obtain the final offset values by minimizing an energy function, consisting of a data term and a smoothness term. Simulation results show that the proposed algorithm improves the picture qualities of backlit images and videos efficiently.
Robust transmission of video sequence over noisy channel using parity-check motion vector

Motion compensation-discrete cosine transform (MC-DCT) coding is an efficient compression technique for digital video sequences. However, the compressed video signal is vulnerable to transmission errors over noisy channels. In this paper, we introduce a novel concept of parity-check motion vector (PMV) into the MC-DCT coder in order to improve its error robustness. By inserting the redundant PMVs systematically into the compressed bitstream, the proposed algorithm is capable of recovering very severe transmission errors, such as loss of an entire frame, in addition to detecting the errors effectively without requesting any information from external devices. The proposed algorithm is implemented based on the H.263 coder, and tested intensively in a realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the conventional H.263 coder in the error prone environment.
Panoramic scene generation from multi-view images with close foreground objects

An algorithm to generate a panorama from multi-view images, which contain foreground objects with varying depths, is proposed in this work. The proposed algorithm constructs a foreground panorama and a background panorama separately, and then merges them into a complete panorama. First, the foreground panorama is obtained by finding the translational displacements of objects between source images. Second, the background panorama is initialized using warped source images and then optimized to preserve spatial consistency and satisfy visual constraints. Then, the background panorama is extended by inserting seams and merged with the foreground panorama. Experimental results demonstrate that the proposed algorithm provides visually satisfying panoramas with all meaningful foreground objects, but without severe artifacts in the backgrounds.
VGEF: Contrast enhancement of dark images using value gap expansion force and sorted histogram equalization

We propose a novel contrast enhancement method for dark images using the value gap expansion force (VGEF) and the sorted histogram equalization. Based on the observation that the inter-pixel relationship is analogous to the electrostatic force, we define the pixel field spread around each pixel and the pixel mass at each pixel position. We compute the VGEF exerted to a pixel by multiplying the pixel field and the pixel mass. Also, we sort the pixels of the same value into 5 clusters according to their VGEF magnitudes to reduce contour artifacts in the enhanced image. Finally, we obtain output pixel values using the transformation function that equalizes the sorted histogram. Experimental results demonstrate that the proposed algorithm effectively enhances the contrast of dark images, and is applicable to the performance improvement of the saliency detection as well.
Optimized Brightness Compensation and Contrast Enhancement for Transmissive Liquid Crystal Displays

An optimized brightness-compensated contrast enhancement (BCCE) algorithm for transmissive liquid crystal displays (LCDs) is proposed in this paper. We first develop a global contrast enhancement scheme to compensate for the reduced brightness when the backlight of an LCD device is dimmed for power reduction. We also derive a distortion model to describe the information loss due to the brightness compensation. Then, we formulate an objective function that consists of the contrast enhancement term and the distortion term. By minimizing the objective function, we maximize the backlight-scaled image contrast, subject to the constraint on the distortion. Simulation results show that the proposed BCCE algorithm provides high-quality images, even when the backlight intensity is reduced by up to 50-70% to save power.
Video Stabilization Based on Feature Trajectory Augmentation and Selection and Robust Mesh Grid Warping

We propose a video stabilization algorithm, which extracts a guaranteed number of reliable feature trajectories for robust mesh grid warping. We first estimate feature trajectories through a video sequence and transform the feature positions into rolling-free smoothed positions. When the number of the estimated trajectories is insufficient, we generate virtual trajectories by augmenting incomplete trajectories using a low-rank matrix completion scheme. Next, we detect feature points on a large moving object and exclude them so as to stabilize camera movements, rather than object movements. With the selected feature points, we set a mesh grid on each frame and warp each grid cell by moving the original feature positions to the smoothed ones. For robust warping, we formulate a cost function based on the reliability weights of each feature point and each grid cell. The cost function consists of a data term, a structure-preserving term, and a regularization term. By minimizing the cost function, we determine the robust mesh grid warping and achieve the stabilization. Experimental results demonstrate that the proposed algorithm reconstructs videos more stably than the conventional algorithms.
Enhanced motion compensation algorithm based on second-order prediction

Several techniques based on the multiple reference frame scheme have been proposed to improve the motion prediction gain. Though these techniques yield higher prediction gain than the single reference frame scheme, they require tremendous computational complexity during the motion search procedure. Besides, blocking artifacts may be visible along the block boundaries, since each macroblock is predicted independently of its neighbors. To overcome these drawbacks, this paper proposes a novel motion compensation algorithm, based on the double reference frame (DRF), the double motion vector (DMV), and the searching position shifting (SPS) schemes. First, to reduce the motion vector bitrate and the computational complexity of motion search procedure, we constrain the number of reference frames to 2, and use only two motion vectors per block. Second, to alleviate the blocking artifacts and to get the better pel prediction, the searching position shifting scheme is introduced. Experimental results demonstrate that the proposed algorithm yields a 3-4 dB higher prediction gain than the single reference frame scheme. The subjective quality is also improved by alleviating the blocking artifacts.
Predictive compression of geometry, color and normal data of 3-D mesh models

Predictive compression algorithms for geometry, color and normal data of three-dimensional (3-D) mesh models are proposed in this work. In order to eliminate redundancies in geometry data, we predict each vertex position by exploiting the position and angle information in neighboring triangles. To compress color data, we propose a mapping table scheme that compresses frequently recurring colors efficiently. For normal data, we propose an average predictor and a 6-4 subdivision quantizer to improve coding gain. Simulation results demonstrate that the proposed algorithm provides better performance than the MPEG-4 standard for 3-D mesh model coding (3-DMC).
Large-Scale 3D Point Cloud Compression Using Adaptive Radial Distance Prediction in Hybrid Coordinate Domains

An adaptive range image coding algorithm for the geometry compression of large-scale 3D point clouds (LS3DPCs) is proposed in this work. A terrestrial laser scanner generates an LS3DPC by measuring the radial distances of objects in a real world scene, which can be mapped into a range image. In general, the range image exhibits different characteristics from an ordinary luminance or color image, and thus the conventional image coding techniques are not suitable for the range image coding. We propose a hybrid range image coding algorithm, which predicts the radial distance of each pixel using previously encoded neighbors adaptively in one of three coordinate domains: range image domain, height image domain, and 3D domain. We first partition an input range image into blocks of various sizes. For each block, we apply multiple prediction modes in the three domains and compute their rate-distortion costs. Then, we perform the prediction of all pixels using the optimal mode and encode the resulting prediction residuals. Experimental results show that the proposed algorithm provides significantly better compression performance on various range images than the conventional image or video coding techniques.
Improving motion picture quality of plasma display panels

An effective algorithm to reduce gray level disturbances in plasma display panels (PDPs) is proposed in this work. Gray level disturbances occur when PDPs display moving image sequences, since each gray level has a different light emission pattern. We design the subfield vector and the driving vectors to minimize the disturbances. First, we employ the lexicographically largest vector as the subfield vector, since it can flexibly control the shapes of light emission patterns. Then, we propose the 1st-order method to determine the driving vectors. Simulation results demonstrate that the proposed algorithm suppresses gray level disturbances effectively and provides faithful image quality.
FDQM: Fast Quality Metric for Depth Maps Without View Synthesis

We propose a fast quality metric for depth maps, called fast depth quality metric (FDQM), which efficiently evaluates the impacts of depth map errors on the qualities of synthesized intermediate views in multiview video plus depth applications. In other words, the proposed FDQM assesses view synthesis distortions in the depth map domain, without performing the actual view synthesis. First, we estimate the distortions at pixel positions, which are specified by reference disparities and distorted disparities, respectively. Then, we integrate those pixel-wise distortions into an FDQM score by employing a spatial pooling scheme, which considers occlusion effects and the characteristics of human visual attention. As a benchmark of depth map quality assessment, we perform a subjective evaluation test for intermediate views, which are synthesized from compressed depth maps at various bitrates. We compare the subjective results with objective metric scores. Experimental results demonstrate that the proposed FDQM yields highly correlated scores to the subjective ones. Moreover, FDQM requires at least 10 times less computations than conventional quality metrics, since it does not perform the actual view synthesis.
Compression of 3-D triangle mesh sequences based on vertex-wise motion vector prediction

We propose an efficient geometry compression algorithm for three-dimensional (3-D) mesh sequences based on the two-stage vertex-wise motion vector (MV) prediction. In general, the MV of a vertex is highly correlated to those of the adjacent vertices. To exploit this high correlation, we define the neighborhood of a vertex, and predict the MV of the vertex from those of the neighborhood. The error vectors are related to the local shape changes of 3-D objects, and still have redundancy. To remove the redundancy, the error vectors are also predicted, at the second stage, spatially or temporally by using a rate-distortion optimization technique. It is shown that the proposed algorithm has simpler structure than the existing segment-based algorithm, and yields better compression performance.
Power-constrained contrast enhancement for OLED displays based on histogram equalization

A novel power-constrained contrast enhancement algorithm for organic light-emitting diode (OLED) displays is proposed in this work. We first develop the log-modified histogram equalization (LMHE) scheme, which reduces overstretching artifacts of the conventional histogram equalization technique. Then, we model the power consumption in OLED displays, and incorporate it into LMHE to achieve the optimal tradeoff between contrast enhancement and power saving. Simulation results demonstrate that the proposed algorithm can reduce the power consumption significantly, while preserving image qualities.
Video saliency detection based on spatiotemporal feature learning

A video saliency detection algorithm based on feature learning, called ROCT, is proposed in this work. To detect salient regions, we design multiple spatiotemporal features and combine those features using a support vector machine (SVM). We extract the spatial features of rarity, compactness, and center prior by analyzing the color distribution in each image frame. Also, we obtain the temporal features of motion intensity and motion contrast to identify visually important motions. We train an SVM classifier using the spatiotemporal features extracted from training video sequences. Finally, we compute the visual saliency of each patch in an input sequence using the trained classifier. Experimental results demonstrate that the proposed algorithm provides more accurate and reliable results of saliency detection than conventional algorithms.
Flexible complexity control between encoder and decoder for video coding

We propose a novel video codec that can distribute computational complexity between the encoder and the decoder in a flexible manner. Since motion estimation is the most computationally intensive part in video coding, it is important to share the motion estimation task. In the proposed algorithm, the decoder estimates motion vectors by performing a partial three step search. The estimated motion vectors are sent back to the encoder via a feedback channel. The encoder then refines the received motion vectors. In this work, four operation modes corresponding to the encoder to decoder complexity ratios are presented. Experimental results demonstrate that the proposed algorithm allocates the complexity between the encoder and the decoder effectively, while providing promising compression performance
Fabrication of Au-Decorated 3D ZnO Nanostructures as Recyclable SERS Substrates

Highly roughened Au-decorated 3D ZnO nano-structures were prepared using a combination of prism holographic lithography and atomic layer deposition techniques. Prism holographic lithography is a simple and rapid method for fabricating ordered 3D nanostructures using the optical interference effects of multiple beams derived from a specially designed prism. Highly ordered reproducible surface-enhanced Raman scattering (SERS) substrates are needed for the reliable calibration of target analyte concentrations. A high density of Au nanoparticles separated by nanoscale gaps was generated on the Au-coated ZnO inverse structures. The nanogaps may function as strong hot spots for highly sensitive SERS-based chemical/biological sensors. The optimized SERS intensity from the prepared Au-coated 3D ZnO inverse structures was 20 times the intensity obtained from an Au-coated flat glass control substrate. The surfaces could be reused after the photocatalytic degradation and removal of adsorbates in the presence of ZnO. The Au-coated 3D ZnO structures described here offer an alternative to traditional single-use SERS substrates.
Robust semi-regular mesh representation of 3D dynamic objects

In this paper, we propose a robust scheme to represent 3D dynamic objects, which are given as a sequence of irregular meshes. The sequence of irregular meshes is converted into a sequence of semi-regular meshes with time-invariant topology to facilitate the manipulation of 3D data. To this end, we sequentially perform the motion estimation of base meshes, the shape transformation of subdivision points, and the selective intra-remeshing. It is shown that a given sequence can be efficiently remeshed without parameterization data by exploiting temporal correlation in the sequence. Simulation results demonstrate that the proposed algorithm reproduces the original geometry faithfully with a sequence of semi-regular meshes.
Multiple random walkers and their application to image cosegmentation

A graph-based system to simulate the movements and interactions of multiple random walkers (MRW) is proposed in this work. In the MRW system, multiple agents traverse a single graph simultaneously. To achieve desired interactions among those agents, a restart rule can be designed, which determines the restart distribution of each agent according to the probability distributions of all agents. In particular, we develop the repulsive rule for data clustering. We illustrate that the MRW clustering can segment real images reliably. Furthermore, we propose a novel image cosegmentation algorithm based on the MRW clustering. Specifically, the proposed algorithm consists of two steps: inter-image concurrence computation and intra-image MRW clustering. Experimental results demonstrate that the proposed algorithm provides promising cosegmentation performance.
Coding Order Decision of B Frames for Rate-Distortion Performance Improvement in Single-View Video and Multiview Video Coding

The coding gain that can be achieved by improving the coding order of B frames in the H.264/AVC standard is investigated in this work. We first represent the coding order of B frames and their reference frames with a binary tree. We then formulate a recursive equation to find out the binary tree that provides a suboptimal, but very efficient, coding order. The recursive equation is efficiently solved using a dynamic programming method. Furthermore, we extend the coding order improvement technique to the case of multiview video sequences, in which the quadtree representation is used instead of the binary tree representation. Simulation results demonstrate that the proposed algorithm provides significantly better R-D performance than conventional prediction structures.
On DCT coefficient distribution in video coding using quad-tree structured partition

The recent image/video coding standards adopt a full quad-tree structured block partitioning with a number of different transform sizes. However, the statistical behavior of the transformed coefficients becomes more difficult to estimate than the previous standards while the single pdf might be enough for modeling the distribution in the previous standards. In this paper, we propose a novel probability density to model the distribution of the transformed coefficients obtained from different transform sizes. The proposed density model is mathematically derived, and the parameters are estimated from previously coded samples. Rate-Distortion (R-D) model is also established from the proposed density model. It is shown in experimental results that the proposed pdf is efficiently used for approximating the rate-distortion (R-D) relation when the different transform sizes are adapted for a coding.
Stitching of heterogeneous images using depth information

We propose a novel heterogeneous image stitching algorithm, which employs disparity information as well as color information. It is challenging to stitch heterogeneous images that have different background colors and diverse foreground objects. To overcome this difficulty, we set the criterion that objects should preserve their shapes in the stitched image. To satisfy this criterion, we derive an energy function using color and disparity gradients. As the gradients are highly correlated with object boundaries, we can find the optimal seam from the energy function, along which two images are pasted. Moreover, we develop a retargeting scheme to reduce the size of the stitched image further. Experimental results demonstrate that the proposed algorithm is a promising tool for stitching heterogeneous images.
Efficient distributed video coding using symmetric motion estimation and channel division

An efficient distributed video coding algorithm using symmetric motion estimation and channel division is proposed in this work. We employ the symmetric motion estimation to generate high quality side information for Wyner-Ziv frames. Also, in the channel division, we classify blocks in the side information into reliable ones and unreliable ones. Then, we transmit parity bits for unreliable blocks only, achieving a coding gain. Simulation results demonstrate that the proposed algorithm provides up to 4 dB better PSNR performance than the conventional distributed video coding algorithms.
Virtual view synthesis using multi-view video sequences

A virtual view synthesis algorithm using multi-view video sequences, which inherits the advantages of both the forward warping and the inverse warping, is proposed in this work. First, we use the inverse warping to synthesize a virtual view without holes from the nearest two views. Second, we detect occluded regions in the synthesized view based on the uniqueness constraint. Then, we refine the occluded regions using the information in the farther views based on the forward warping technique. Simulation results demonstrate that the proposed algorithm provides significantly higher PSNR performances than the conventional inverse warping scheme.
Depth-guided adaptive contrast enhancement using 2D histograms

A novel contrast enhancement (CE) algorithm using 2-dimensional (2D) histograms, which transforms pixel values adaptively based on the depth information, is proposed in this work. In general, foreground objects convey more important visual information than background regions. Hence we assign high CE priorities to foreground pixels using the depth values and generate a depth-guided 2D histogram. Then, we stretch the gray-level differences of adjacent foreground pixels more strongly than those of adjacent background pixels. Moreover, to enhance background regions as well, we design two transformation functions for the foreground and the background separately. By combining the two functions according to pixel depths, we obtain an adaptive space-variant transformation function, which is finally used to reconstruct the output image. Experimental results show that the proposed algorithm outperforms conventional CE algorithms by enhancing salient foreground objects efficiently and preserving background details faithfully.
Contrast enhancement based on layered difference representation

A novel contrast enhancement algorithm based on the layered difference representation is proposed in this work. We first represent gray-level differences at multiple layers in a tree-like structure. Then, based on the observation that gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image, we solve a constrained optimization problem to derive the transformation function at each layer. Finally, we aggregate the transformation functions at all layers into the overall transformation function. Simulation results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.