nils-herrmann/clean_scirepeval huggingface dataset:
Error resilient coding of 3D meshes

Compressed bit streams of 3D mesh data are highly vulnerable to transmission errors due to predictive coding and entropy coding. In this work, we propose an error resilient and progressive coding scheme for 3D meshes. To make a compressed bit stream robust to transmission errors, 3D mesh data is partitioned into several segments, which are then encoded independently. In the decoder, a novel error concealment scheme is employed to improve the visual quality of corrupted segments. Simulation results show that the proposed algorithm provides a good quality reconstruction even in severe error conditions.
Graph Theoretical Optimization of Prediction Structure in Multiview Video Coding

An algorithm to construct the optimal prediction structure in multiview video coding (MVC) is proposed in this work. We employ the graph theory as a framework. By considering each frame as a vertex and the motion compensation or disparity compensation as an edge, we represent a prediction structure as a spanning tree. Then, we obtain the optimal structure by finding the minimum spanning tree using the Prim's algorithm. Simulation results demonstrate that the proposed algorithm provides about 0.2-0.4 dB better PSNR performance than the conventional prediction structure, and about 1.5 dB better performance than the simulcast.
Image Enhancement using Sorted Histogram Specification and POCS Postprocessing

An image enhancement algorithm based on the sorted histogram equalization (SHE) and specification (SHS) is proposed in this work. Although SHE and SHS can generate an arbitrary output histogram exactly, they yield false contour artifacts and amplify noises in many cases. To reduce these artifacts, we propose a postprocessing scheme using the projection onto convex sets (POCS) theory. Specifically, the proposed algorithm iteratively processes the output images of SHE and SHS using the low-pass, boundary and dithering conditions. Simulation results demonstrate that the proposed algorithm provides an excellent image quality, by improving the contrast while reducing undesired artifacts.
Multiscale Saliency Detection Using Random Walk With Restart

In this paper, we propose a graph-based multiscale saliency-detection algorithm by modeling eye movements as a random walk on a graph. The proposed algorithm first extracts intensity, color, and compactness features from an input image. It then constructs a fully connected graph by employing image blocks as the nodes. It assigns a high edge weight if the two connected nodes have dissimilar intensity and color features and if the ending node is more compact than the starting node. Then, the proposed algorithm computes the stationary distribution of the Markov chain on the graph as the saliency map. However, the performance of the saliency detection depends on the relative block size in an image. To provide a more reliable saliency map, we develop a coarse-to-fine refinement technique for multiscale saliency maps based on the random walk with restart (RWR). Specifically, we use the saliency map at a coarse scale as the restarting distribution of RWR at a fine scale. Experimental results demonstrate that the proposed algorithm detects visual saliency precisely and reliably. Moreover, the proposed algorithm can be efficiently used in the applications of proto-object extraction and image retargeting.
Progressive coding of 3D dynamic mesh sequences using spatiotemporal decomposition

We propose an algorithm that compresses a semiregular mesh sequence of three dimensional dynamic objects using the spatiotemporal wavelet transform. By exploiting the regular structure of the semi-regular mesh sequence, we develop an embedded coding scheme, which supports both SNR and temporal scalability modes. Simulation results demonstrate that the proposed algorithm provides significantly better compression performance than the static mesh coder, which encodes each frame independently.
Rate-Distortion Optimized Image Compression Using Generalized Principal Component Analysis

A novel image compression algorithm based on generalized principal component analysis (GPCA) is proposed in this work. Each image block is first classified into a subspace and is represented with a linear combination of the basis vectors for the subspace. Therefore, the encoded information consists of subspace indices, basis vectors and transform coefficients. We adopt a vector quantization scheme and a predictive partial matching scheme to encode subspace indices and basis vectors, respectively. We also propose a rate-distortion optimized quantizer to encode transform coefficients efficiently. Simulation results demonstrate that the proposed algorithm provides better compression performance than JPEG, especially at low bitrates
Reduction of gray level disturbances in plasma display panels

An effective method to reduce gray level disturbances in plasma display panels (PDPs) is proposed in this work. First, we develop a systematic model for gray level disturbances, which occur when PDPs display moving image sequences. Then, we derive an ideal condition for the disturbances removal. Based on the condition, we propose the optimal subfield and driving vectors to minimize the disturbances. Simulation results show that the proposed algorithm provides a good moving image quality.
POD: Discovering Primary Objects in Videos Based on Evolutionary Refinement of Object Recurrence, Background, and Primary Object Models

A primary object discovery (POD) algorithm for a video sequence is proposed in this work, which is capable of discovering a primary object, as well as identifying noisy frames that do not contain the object. First, we generate object proposals for each frame. Then, we bisect each proposal into foreground and background regions, and extract features from each region. By superposing the foreground and background features, we build the object recurrence model, the background model, and the primary object model. We develop an iterative scheme to refine each model evolutionarily using the information in the other models. Finally, using the evolved primary object model, we select candidate proposals and locate the bounding box of a primary object by merging the proposals selectively. Experimental results on a challenging dataset demonstrate that the proposed POD algorithm extracts primary objects accurately and robustly.
Semi-regular mesh representation of 3D dynamic objects based on correspondence matching

In this paper, we propose a novel method to represent 3D dynamic objects, which are captured with a laser scanner in successive time instances. The objective is to convert an input mesh sequence into a semi-regular mesh sequence with time-invariant topology information, since it enables the easier manipulation of 3D data using many signal processing techniques. We achieve this objective by performing global motion estimation, local deformation estimation, and correspondence optimization subsequently. Simulation results show that the proposed algorithm reconstructs the original geometry faithfully. Furthermore, the resulting semi-regular mesh sequences can be effectively compressed by adopting wavelet coding schemes.
Efficient Multi-Hypothesis Error Concealment Technique for H.264

An efficient multi-hypothesis error concealment algorithm for H.264 video is proposed in this work. The proposed algorithm temporally conceals a lost block by combining several hypothesis blocks in the previous frame. We investigate the error recovery performance according to the number of hypotheses and the weighting coefficients. Simulation results demonstrate that the proposed algorithm provides better performance than the conventional error concealment algorithm, although the additional complexity requirement is negligible.
Efficient depth map recovery using concurrent object boundaries in texture and depth images

An efficient depth map recovery algorithm, using concurrent object boundaries in texture and depth signals, is proposed in this work. We first analyze the effects of a distorted depth map on the qualities of synthesized views. Based on the analysis, we propose an object boundary detection scheme to restore sharp boundaries from a distorted depth map. Specifically, we initially estimate object boundaries from a depth map using the gradient magnitude at each pixel. We then multiply the gradient magnitudes of texture and depth pixels. Then, we suppress boundary pixels with non-maximum magnitudes and refine the object boundaries. Finally, we filter depth pixels along the gradient orientations using a median filter. Experimental results show that the proposed algorithm significantly improves the qualities of synthesized views, as compared with conventional algorithms.
Progressive compression of 3D triangular meshes using topology-based Karhunen-Loève transform

In this work, we propose a progressive compression algorithm using topology-based Karhunen-Loeve transform(KLT). First, we simplify an input mesh to represents an original mesh in several level of details. Then, coordinates of decimated vertices at each level are predicted from the coarser level mesh, and the prediction residuals are transmitted to a decoder. To provide high coding efficiency, we apply the topology-based KLT, which compacts the energy into a few coefficients, to the prediction residuals. Moreover, we develop a bit plane coder, which uses a context-adaptive arithmetic coder, for the entropy coding. Experiments on various 3D meshes show that the proposed algorithm provides enhanced compression performance.
Fractal coding of video sequence using circular prediction mapping and noncontractive interframe mapping

We propose a novel algorithm for fractal video sequence coding, based on the circular prediction mapping and the noncontractive interframe mapping. The proposed algorithm can effectively exploit the temporal correlation in real image sequences, since each range block is approximated by the domain block in the adjacent frame, which is of the same size as the range block. The computer simulation results demonstrate that the proposed algorithm provides very promising performance at low bit rate, ranging from 40-250 kbyte/s.
Reliable optical flow estimation in motion-blurred regions

A robust optical flow estimation algorithm for motion-blurred regions is proposed in this work. We first obtain initial optical flow vectors. Then, we detect motion-blurred regions that yield low contrast, low saturation, and inconsistent optical flow vectors. We replace the optical flow vectors at motion-blurred pixels with reliable vectors at nearby unblurred pixels. To this end, we develop an energy minimization framework. Simulation results demonstrate that the proposed algorithm refines optical flow vectors in motion-blurred regions accurately and provides better performance than conventional algorithms.
Bit Allocation Algorithm With Novel View Synthesis Distortion Model for Multiview Video Plus Depth Coding

An efficient bit allocation algorithm based on a novel view synthesis distortion model is proposed for the rate-distortion optimized coding of multiview video plus depth sequences in this paper. We decompose an input frame into nonedge blocks and edge blocks. For each nonedge block, we linearly approximate its texture and disparity values, and derive a view synthesis distortion model, which quantifies the impacts of the texture and depth distortions on the qualities of synthesized virtual views. On the other hand, for each edge block, we use its texture and disparity gradients for the distortion model. In addition, we formulate a bit-rate allocation problem in terms of the quantization parameters for texture and depth data. By solving the problem, we can optimally divide a limited bit budget between the texture and depth data, in order to maximize the qualities of synthesized virtual views, as well as those of encoded real views. Experimental results demonstrate that the proposed algorithm yields the average PSNR gains of 1.98 and 2.04 dB in two-view and three-view scenarios, respectively, as compared with a benchmark conventional algorithm.
3D mesh compression based on dual-ring prediction and MMSE prediction

A three-dimensional (3D) mesh compression algorithm based on novel prediction methods and a mode decision scheme is proposed in this work. After decomposing an input mesh into base and refinement layers, we segment the geometry data of each layer into clusters. To encode vertex positions efficiently, we propose two prediction methods: the dual ring prediction and the minimum mean square error (MMSE) prediction. Also, we develop a mode decision scheme that selects the best prediction mode for each cluster. Simulation results demonstrate that the proposed algorithm provides significantly better compression performance than conventional techniques.
Progressive encoding of voxel surfaces based on pattern code representation

We propose a progressive encoding algorithm for 3D voxel surfaces, based on the pattern code representation (PCR). In PCR, a voxel surface is represented by a series of pattern codes. It can achieve coding gain, since the pattern codes are highly correlated. In the multi-resolution framework, the coding gain can be further improved by the decimation constraint from the lower resolution surface. Furthermore, an alternative patternbook is adopted for voxels within smooth regions to reduce the number of possible pattern codes. Simulation results show that the proposed algorithm provides better performance than the conventional PCR algorithm.
Progressive mesh compression using cosine index predictor and 2-stage geometry predictor

In this paper, we propose a progressive compression algorithm for three-dimensional (3D) mesh data, which can efficiently encode the connectivity and geometry information. Using cosine index predictor and vertex confinement rule, the proposed scheme encodes the connectivity information compactly. For the geometry-information coding, a 2-stage geometry predictor is proposed to reduce the prediction error more effectively than the conventional techniques. Simulation results show that the proposed algorithm yields better coding performance than the conventional algorithm.
Multiple description coding of motion fields for robust video transmission

In many video-coding standards, the motion vector field is one of the most important data in the compressed bitstream, and its loss can lead to severe degradation in the decoded picture quality. We propose the multiple description motion coding (MDMC) algorithm to enhance the robustness of the motion vector field against transmission errors. In MDMC, the motion vector field is encoded into two descriptions, which are transmitted over distinct channels to the decoder. The decoder is designed to provide an acceptable quality prediction image, even if one of the descriptions is lost during the transmission. Moreover, the decoder can reconstruct a higher quality prediction image, when both the descriptions are received without error. A complete multiple description video coder, based on the MDMC, is implemented by modifying the syntax of the H.263 standard, 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 H.263 coder in the error-prone environment.
Spatiotemporal saliency detection for video sequences based on random walk with restart.

A novel saliency detection algorithm for video sequences based on the random walk with restart (RWR) is proposed in this paper. We adopt RWR to detect spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution using the features of motion distinctiveness, temporal consistency, and abrupt change. Among them, the motion distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal saliency distribution as a restarting distribution of the random walker. In addition, we design the transition probability matrix for the walker using the spatial features of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the steady-state distribution of the walker. The proposed algorithm detects foreground salient objects faithfully, while suppressing cluttered backgrounds effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically. Experimental results on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms qualitatively and quantitatively.
Frame loss concealment for stereoscopic video plus depth sequences

In this work, an efficient frame loss concealment algorithm for stereoscopic video plus depth sequences is proposed. To conceal an erroneous left color frame, we extrapolate motion vectors for the temporal error concealment. To conceal a left depth frame, we exploit the motion vectors of the corresponding left color frame. To conceal a right color frame, we employ a 3D image warping technique to determine matching pixels between inter-views, and perform the reconstruction based on the similarities of the motion vectors and the intensity differences of matching pixels. Finally, to conceal a right depth frame, we use the motion vectors of the right color frame and the left color frame. Simulation results show that the proposed algorithm is more effective than conventional algorithms in recovering erroneous color and depth frames. Moreover, it is also demonstrated that the proposed algorithm can be used for efficient virtual view synthesis in error prone environments.
Real-time segmentation of objects from video sequences with non-stationary backgrounds using spatio-temporal coherence

A real-time video segmentation algorithm, which can extract objects from video sequences even with non-stationary backgrounds, is proposed in this work. First, we segment the first frame into an object and a background interactively to build the probability density functions of colors in the object and the background. Then, for each subsequent frame, we construct a coherence strip, which is likely to contain the object contour, by exploiting spatio-temporal correlations. Finally, we perform the segmentation by minimizing an energy function composed of color, coherence, and smoothness terms. Experimental results on various test sequences show that the proposed algorithm provides accurate segmentation results in real-time, even though video sequences contain unstable camera motions.
Feature-preserving thumbnail generation based on graph cuts

A novel algorithm for thumbnail generation, which preserves characteristic features of a source image including blurs and textures, is proposed in this work. When a source image is subsampled to generate a thumbnail, important visible cues, such as blurs and noises, are lost. To overcome this drawback, we first create multiple thumbnail candidates that accentuate three classes of image features: focal blur, motion blur, and detail. Then, we obtain the final thumbnail by composing these candidates adaptively. Assuming that image features are spatially varying but locally static, we formulate the composition task as a labeling problem, and employ the graph-cut optimization technique to solve the problem. Simulation results demonstrate that the proposed algorithm provides feature-preserving thumbnails efficiently.
Single-image deraining using an adaptive nonlocal means filter

An adaptive rain streak removal algorithm for a single image is proposed in this work. We observe that a typical rain streak has an elongated elliptical shape with a vertical orientation. Thus, we first detect rain streak regions by analyzing the rotation angle and the aspect ratio of the elliptical kernel at each pixel location. We then perform the nonlocal means filtering on the detected rain streak regions by selecting nonlocal neighbor pixels and their weights adaptively. Experimental results demonstrate that the proposed algorithm removes rain streaks more efficiently and provides higher restored image qualities than conventional algorithms.
3D mesh compression using triangle fan structure

In this paper, we propose an efficient compression algorithm for 3D triangular mesh, which is composed of connectivity data and geometry data. Firstly, a vertex degree warping method is proposed to compress the connectivity data losslessly. While the connectivity data and the geometry data are encoded independently in most conventional algorithms, the geometrical information is exploited to efficiently compress the connectivity data in the proposed approach. Secondly, a dual parallelogram prediction technique is proposed as an effective geometry prediction scheme. By adopting the triangle fan structure, the proposed algorithm provides a smaller prediction error than the conventional algorithm. Simulation results for various 3D mesh models demonstrate that the proposed algorithm yields a higher compression ratio than the conventional algorithm.
Power-Constrained Contrast Enhancement for Emissive Displays Based on Histogram Equalization

A power-constrained contrast-enhancement algorithm for emissive displays based on histogram equalization (HE) is proposed in this paper. We first propose a log-based histogram modification scheme to reduce overstretching artifacts of the conventional HE technique. Then, we develop a power-consumption model for emissive displays and formulate an objective function that consists of the histogram-equalizing term and the power term. By minimizing the objective function based on the convex optimization theory, the proposed algorithm achieves contrast enhancement and power saving simultaneously. Moreover, we extend the proposed algorithm to enhance video sequences, as well as still images. Simulation results demonstrate that the proposed algorithm can reduce power consumption significantly while improving image contrast and perceptual quality.
SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking

A simple yet effective object descriptor for visual tracking is proposed in this paper. We first decompose the bounding box of a target object into multiple patches, which are described by color and gradient histograms. Then, we concatenate the features of the spatially ordered patches to represent the object appearance. Moreover, to alleviate the impacts of background information possibly included in the bounding box, we determine patch weights using random walk with restart (RWR) simulations. The patch weights represent the importance of each patch in the description of foreground information, and are used to construct an object descriptor, called spatially ordered and weighted patch (SOWP) descriptor. We incorporate the proposed SOWP descriptor into the structured output tracking framework. Experimental results demonstrate that the proposed algorithm yields significantly better performance than the state-of-the-art trackers on a recent benchmark dataset, and also excels in another recent benchmark dataset.
Fast background subtraction algorithm using two-level sampling and silhouette detection

An efficient background subtraction algorithm using two-level sampling and silhouette detection is proposed in this work. In the two-level sampling, we identify moving objects at the block level and then at the pixel level. Then, in the silhouette detection, around each sampled foreground pixel, we refine the shapes of foreground objects. We also develop two fast modes for the silhouette detection, which utilizes the spatio-temporal coherence of moving foreground objects. Simulation results demonstrate that the proposed algorithm provides accurate segmentation results without flickering artifacts, while requiring a low computational load.