nils-herrmann/clean_scirepeval huggingface dataset:
H.264/Advanced Video Control Perceptual Optimization Coding Based on JND-Directed Coefficient Suppression

The field of video coding has been exploring the compact representation of video data, where perceptual redundancies in addition to signal redundancies are removed for higher compression. Many research efforts have been dedicated to modeling the human visual system's characteristics. The resulting models have been integrated into video coding frameworks in different ways. Among them, coding enhancements with the just noticeable distortion (JND) model have drawn much attention in recent years due to its significant gains. A common application of the JND model is the adjustment of quantization by a multiplying factor corresponding to the JND threshold. In this paper, we propose an alternative perceptual video coding method to improve upon the current H.264/advanced video control (AVC) framework based on an independent JND-directed suppression tool. This new tool is capable of finely tuning the quantization using a JND-normalized error model. To make full use of this new rate distortion adjustment component the Lagrange multiplier for rate distortion optimization is derived in terms of the equivalent distortion. Because the H.264/AVC integer discrete cosine transform (DCT) is different from classic DCT, on which state-of-the-art JND models are computed, we analytically derive a JND mapping formula between the integer DCT domain and the classic DCT domain which permits us to reuse the JND models in a more natural way. In addition, the JND threshold can be refined by adopting a saliency algorithm in the coding framework and we reduce the complexity of the JND computation by reusing the motion estimation of the encoder. Another benefit of the proposed scheme is that it remains fully compliant with the existing H.264/AVC standard. Subjective experimental results show that significant bit saving can be obtained using our method while maintaining a similar visual quality to the traditional H.264/AVC coded video.
Person tracking with partial occlusion handling

Occlusion is a challenge for tracking especially in dynamic scene. It adds the consideration for background modeling. In the condition, the tracker will be influenced by both occlusions and background. In this paper, we address the problem by proposing a robust algorithm based on improved particle filter using discriminative model without background modeling. Discriminative model offers accurate templates for occlusion detection by alleviating influence from background pixels. Since particle filter cannot carry out effective tracking under heavy occlusion, blocking is introduced to solve the problem by abandoning unobservable parts of the target. Experimental results show that our algorithm can work persistently and effectively under severe occlusion even in dynamic scene compared with state-of-the-arts.
Symmetric Cluster Set Level of Detail for Real-Time Terrain Rendering

In this paper, we present an improvement for batch-based quad tree terrain rendering that drastically reduces the number of draw calls to the graphics processing unit. As a result, more fine-grained triangular optimization is possible without sacrificing triangle throughput. No extra preprocessing is required. In general, quad tree terrain algorithms recursively subdivide mesh geometry to meet visual error constraints. Batch-based techniques use buffered grid blocks as the subdivision primitive for better triangle throughput. We base our algorithm on structural observations of such terrain quad trees. First, we show that the four sub-nodes of any non-leaf can be categorized into sixteen distinct states of drawing behavior. These states are symmetric in such a way that allows just five unique geometries to represent all of them. With the additional observation that leaf nodes appear in groups of four across regions of homogeneous grid resolution, we develop a technique employing 23 unique geometric batches from which any terrain can be rendered. The resulting algorithm reliably reduces draw calls by a factor of 6 on average, and achieves render performance 30 to 50 percent faster than comparable techniques.
A Context-Adaptive Prediction Scheme for Parameter Estimation in H.264/AVC Macroblock Layer Rate Control

In this paper, we present a novel context-adaptive model parameter prediction scheme for improving the estimation accuracy of the mean absolute difference (MAD) of texture and other model parameters in the linear rate quantization (R-Q) model-based H.264/AVC macroblock layer rate control for low bit rate real-time applications. The context-adaptive prediction scheme simultaneously exploits both spatial correlations and temporal correlations among the neighboring macroblocks within a so-called context of a macroblock. The location and shape of the context as well as the number of neighboring macroblocks in the context are adaptively computed according to local video signal characteristics using a Manhattan distance metric and an improved 2-D sliding window method. The proposed context-adaptive model parameter prediction scheme effectively suppresses the detrimental oscillations of estimated model parameters. Extensive experiments show that compared to the recent H.264/AVC reference software, macroblock layer rate control algorithm using our proposed context-adaptive prediction scheme significantly improves the MAD and model parameter prediction accuracy and bit achievement accuracy, and hence obtains much better rate distortion performance.
Fast segment-wise DC coding for 3D video compression

A Fast Segment-wise DC Coding (SDC) algorithm is proposed for 3D-HEVC depth intra coding. Since SDC is a supplementary option for intra prediction modes, it is not necessary to check SDC option for all intra candidate modes during full Rate-Distortion (RD) cost calculation. Based on the observation that most Prediction Units (PUs) choose the same intra coding mode with and without SDC option, we propose a fast SDC coding algorithm to avoid unnecessary full-RD cost calculation. The performance evaluation is based on HTM-11.0 and the experimental result for the proposed fast algorithm shows up to 25% encoding time saving with only 0.19% BD-bitrate increase in coded and synthesized view for All-Intra test case.
A novel dual-path architecture for HDTV video decoding

We present an architecture for digital HDTV video decoding (MPEG-2 MP@HL), based on dual decoding data paths controlled in a block layer synchronization manner and an efficient write back scheme. Our fixed schedule controller synchronizes the baseline units on a block basis in both data-paths. This scheme reduces embedded buffer sizes within the decoder and eliminates a lot of external memory bus contentions. In our write back scheme, the display DRAM is physically separated from the anchor picture DRAM, and is added to the display engine, not to the bus. The slight increase in overall DRAM size is acceptable due to the low DRAM cost today. This improves the parallelism in accessing anchor and display pictures and saves about 80 clock cycles per macroblock (based on a 81 MHz clock). Compared to the other decoding approaches such as the slice bar decoding method and the crossing-divided method, this scheme reduces memory access contentions and the amount of embedded local memory required. Our simulations show that with a relatively low speed 81 MHz clock, our architecture uses fewer than the 332 cycles (required real-time decoding upper bound), to decode each macroblock, without a high cost in overall chip area.
A Bus-monitoring Model For MPEG Decoder Design

In MPEG video decoder design, the variety of macroblock types and variable-length codes has increased the difJiculty in predicting memoiy accesses and bus utilization pattern for accessing compressed data or pixels toJroom external RAMS during the decoding process. In this paper, we present a model and a simulator to aid MPEG decoder design by providing useful statistics related to bus utilization and waiting cycles. The model explores MPEG decoder architecture and data nature; it provides results to optimize bus and buffer design in MPEG decoding. Simulation is peformed to test out different bus arbitration schemes for MP@ML data in MPEG-2 decoding.
Compression of HD videos by a contrast-based human attention algorithm

The emergence of social networks combined with the prevalence of mobile technology has led to an increasing demand of high definition video transmission and storage. One of the challenges of video compression is the ability to reduce the video size without significant visual quality loss. In this paper, we propose a new method that achieves compression reduction levels ranging from 2.6% to 16.9% while maintaining or improving subjective quality. Precisely, our approach is a saliency-aware mechanism that predicts and classifies regions-of-interests (ROIs) of a typical human eye gaze according to the static attention model (SAM) from the human visual system (HVS). We coin the term contrast human attention regions of interest (Contrast-HAROIs) to refer to those identified regions. Finally, we reduce the data load of those non Contrast-HAROIs via a smoothing spatial filter. Experimental results carried on eight sequences show that our technique reduces the size of HD videos further than the standard H.264/AVC. Moreover, it is in average 30% times faster than another saliency and motion aware algorithm.
Object boundary-based synthesized view distortion estimation

In this paper, we propose an object boundary based view synthesis distortion estimation (VSDE) for depth map coding to improve virtual synthesized view quality. The proposed method estimates depth map distortion related to an energy minimization in such a way that additional sharp edge information of depth maps involves in VSDE. It aims at reducing visual artifacts of synthesized views along sharp edge areas while maintaining coding efficiency of depth map coding. As a result, it improves the depth coding performance slightly and reduces rendering distortions around the object boundaries. Experimental results show that the proposed method shows improved subjective visual quality while reducing coding redundancy.
Uniform probability model for deriving intra prediction angular table in High Efficiency Video Coding

High Efficiency Video Coding (HEVC) is the latest international standard of video coding under development. In HEVC, the new technology of intra prediction, Unified Directional Intra (UDI), has shown significant improvement on coding performance. Comparing to the intra prediction of H.264/AVC, significant performance improvement was observed for all intra configured tests. In the HEVC Test Model (HM), a predefined angular table is used for generating predicted sample values of UDI. However, how the table is obtained was not clearly presented in the literature. This paper gives a detailed derivation and explanation based on a novel uniform probability model. With this novel model, angular table derivation method was investigated and the selection of table entries used in UDI prediction is well explained. Simulation results show that using the improved angular table proposed in this paper, Bjontegaard Delta (BD)-rate improvement is achieved for sequences with large amount of various textures, in both all intra high efficiency and low complexity configurations.
Enhanced Linear R-Q Model Based Rate Control for H.264/AVC Using Context-Adaptive Parameter Estimation

We present an efficient and accurate macroblock layer rate control for H.264/AVC using a context-adaptive parameter estimation scheme. Our enhanced linear rate quantization (R-Q) model based rate control, which simultaneously controls texture and non-texture bits, is developed using an approximation of the theoretical rate distortion (R-D) function. A second key component is a novel context-adaptive prediction model for improving the estimation accuracy of both the mean absolute difference (MAD) of texture bits and the other model parameters. This prediction model maintains high spatial and temporal correlations among the neighboring macroblocks within the context using a distance metric and an improved sliding window. Compared to JM 12.2, the proposed algorithm achieves up to 0.98 dB improvements in PSNR and bit mismatch has also been significantly reduced.
Memory reduction by Haar wavelet transform for MPEG decoder

In MPEG-2 HDTV, the bandwidth and memory size requirements are very large. We present a simple algorithm that requires low complexity hardware for the MPEG-2 decoder memory reduction. Our algorithm is based on the Haar wavelet transform (HWT), with no multipliers needed. Following the HWT, we use a variable-length coder to compress the data before storing them in the memory for future references. Our technique cuts down the memory requirement from 12 Mbytes to 7 Mbytes for the 4:2:0 HDTV application.
A novel VQ codebook design technique

In this paper, we present an approach to increase the amount of VQ codebook patterns to improve image quality without a large overhead on the codebook memory and search time. Basically, our approach characterizes a pattern into a key value, which is used in the codebook search; "reflection operation" was performed on codewords using the key values, to get more patterns to improve image quality. Our basic VQ system is a 2-step VQ where VQ is used to compress prediction errors resulting from simple scalar quantization of pixel blocks. The codebook search time for our technique is O(logN+K) for each input vector, where N is the codebook size and K is the Voronoi range. Experimental results are presented in the paper.
Harmonic Active Contours

We propose a segmentation method based on the geometric representation of images as 2-D manifolds embedded in a higher dimensional space. The segmentation is formulated as a minimization problem, where the contours are described by a level set function and the objective functional corresponds to the surface of the image manifold. In this geometric framework, both data-fidelity and regularity terms of the segmentation are represented by a single functional that intrinsically aligns the gradients of the level set function with the gradients of the image and results in a segmentation criterion that exploits the directional information of image gradients to overcome image inhomogeneities and fragmented contours. The proposed formulation combines this robust alignment of gradients with attractive properties of previous methods developed in the same geometric framework: 1) the natural coupling of image channels proposed for anisotropic diffusion and 2) the ability of subjective surfaces to detect weak edges and close fragmented boundaries. The potential of such a geometric approach lies in the general definition of Riemannian manifolds, which naturally generalizes existing segmentation methods (the geodesic active contours, the active contours without edges, and the robust edge integrator) to higher dimensional spaces, non-flat images, and feature spaces. Our experiments show that the proposed technique improves the segmentation of multi-channel images, images subject to inhomogeneities, and images characterized by geometric structures like ridges or valleys.
Efficient Algorithm for Level Set Method Preserving Distance Function

The level set method is a popular technique for tracking moving interfaces in several disciplines, including computer vision and fluid dynamics. However, despite its high flexibility, the original level set method is limited by two important numerical issues. First, the level set method does not implicitly preserve the level set function as a distance function, which is necessary to estimate accurately geometric features, s.a. the curvature or the contour normal. Second, the level set algorithm is slow because the time step is limited by the standard Courant-Friedrichs-Lewy (CFL) condition, which is also essential to the numerical stability of the iterative scheme. Recent advances with graph cut methods and continuous convex relaxation methods provide powerful alternatives to the level set method for image processing problems because they are fast, accurate, and guaranteed to find the global minimizer independently to the initialization. These recent techniques use binary functions to represent the contour rather than distance functions, which are usually considered for the level set method. However, the binary function cannot provide the distance information, which can be essential for some applications, s.a. the surface reconstruction problem from scattered points and the cortex segmentation problem in medical imaging. In this paper, we propose a fast algorithm to preserve distance functions in level set methods. Our algorithm is inspired by recent efficient l1 optimization techniques, which will provide an efficient and easy to implement algorithm. It is interesting to note that our algorithm is not limited by the CFL condition and it naturally preserves the level set function as a distance function during the evolution, which avoids the classical re-distancing problem in level set methods. We apply the proposed algorithm to carry out image segmentation, where our methods prove to be 5-6 times faster than standard distance preserving level set techniques. We also present two applications where preserving a distance function is essential. Nonetheless, our method stays generic and can be applied to any level set methods that require the distance information.
Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.
Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.
Robust Principal Component Analysis on Graphs

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first issue with a sparse penalty term. The second issue can be handled with the matrix factorization model, which is however non-convex. Besides, PCA based clustering can also be enhanced by using a graph of data similarity. In this article, we introduce a new model called 'Robust PCA on Graphs' which incorporates spectral graph regularization into the Robust PCA framework. Our proposed model benefits from 1) the robustness of principal components to occlusions and missing values, 2) enhanced low-rank recovery, 3) improved clustering property due to the graph smoothness assumption on the low-rank matrix, and 4) convexity of the resulting optimization problem. Extensive experiments on 8 benchmark, 3 video and 2 artificial datasets with corruptions clearly reveal that our model outperforms 10 other state-of-the-art models in its clustering and low-rank recovery tasks.
A priori information in image segmentation: energy functional based on shape statistical model and image information

In this paper, we propose an energy functional to segment objects whose global shape is a priori known thanks to a statistical model. Our work aims at extending the variational approach of Chen et al. [Y. Chen, et al., 2002] by integrating the statistical shape model of Leventon et al. [M. Leventon, et al., 2000]. The proposed energy functional allows us to capture an object that exhibits high image gradients and a shape compatible with the statistical model which best fits the segmented object. The minimization of the functional provides a system of coupled equations whose steady-state solution is the solution of the segmentation problem. Results are presented on synthetic and medical images.
An efficient segmentation method for ultrasound images based on a semi-supervised approach and patch-based features

Segmenting ultrasound images is a challenging problem where standard unsupervised segmentation methods such as the well-known Chan-Vese method fail. We propose in this paper an efficient segmentation method for this class of images. Our proposed algorithm is based on a semi-supervised approach (user labels) and the use of image patches as data features. We also consider the Pearson distance between patches, which has been shown to be robust w.r.t speckle noise present in ultrasound images. Our results on phantom and clinical data show a very high similarity agreement with the ground truth provided by a medical expert.