nils-herrmann/clean_scirepeval huggingface dataset:
Preprocessing method for region-of-interest video call compression

This paper presents a new region-of-interest(ROI) based compression scheme for video call applications in a preprocessing manner. In our application, ROI is the human region which is detected by a human/background segmentation method, and perceptually less important details are removed by applying the edge-preserving spatio-temporal filter to the background regions. In order to obtain the accurate ROI rapidly, we develop a block-wise segmentation scheme and then ROI is decided stochastically. Since our method removes details in the background regions except the edges, we can achieve higher compression rates without sacrificing subjective qualities. Moreover, our method can be applied to any kind of codecs because it works as a pre-processing step. Experimental results show that our method gives accurate ROI and yields similar peak signal to noise ratio (PSNR) in the ROI while achieving much higher compression ratio than the conventional compression methods.
H.264/AVC based color filter array compression with inter-channel prediction model

In the case of images captured by color filter array (CFA), the compression-first approach (compression of CFA) is shown to be better than the demosaick-first approach (general image compression). In this paper, we propose a new H.264/AVC based compression-first approach that exploits the correlation between the color channels. For this, we propose an inter-channel prediction model described by a linear equation in the RGB space. Specfically, the R and B pixels are predicted by a neighboring G pixel or demosaicked G pixel at the same location. The proposed prediction scheme further reduces the variance of prediction error, and as a result the proposed method provides about 1.45dB gain on average and 2.2dB gain at maximum for the green channel over the conventional compression-frist scheme. For the non-green channel, average gain is about 1.0dB and maximum gain is about
DCT algorithms for VLSI parallel implementations

Two algorithms are presented for computing the discrete cosine transform (DCT) on existing VLSI structures. First, it is shown that the N-point DCT can be implemented on the existing systolic architecture for the N-point discrete Fourier transform (DFT) by introducing some modifications. Second, a new prime factor DCT algorithm is presented for the class of DCTs of length N=N/sub 1/*N/sub 2/, where N/sub 1/ and N/sub 2/ are relatively prime and odd numbers. It is shown that the proposed algorithm can be implemented on the already existing VLSI structures for prime factor DFT. The number of multipliers required is comparable to that required for the other fast DCT algorithms. It is shown that the discrete sine transform (DST) can be computed by the same structure.
Intra prediction method based on the linear relationship between the channels for YUV 4∶2∶0 intra coding

In general, the transformation from RGB to YUV color space reduces the correlation between the channels. But some sources in the YUV space still have strong inter-channel correlation, which can be modeled as a linear function. Based on this linear model, we propose a new intra chrominance prediction method for color image compression in YUV 4∶2∶0 color space. A block of chrominance pixels to be encoded is predicted from the reconstructed luminance signal using the proposed prediction scheme and the residual is encoded based on the H.264/AVC. Also, an implicit prediction method that can obviate the transmission of side information is proposed. The experimental results show that there are about 0.35∼1.0dB gains for the luminance (Y) channel, and 0.5∼3.0dB gains for the chrominance (Cb or Cr) channels at the medium to high bit-rates. The proposed method also shows some gains at the low bit-rates.
Narrow-band interference suppression in direct sequence spread spectrum systems using a lattice IIR notch filter

This paper proposes an algorithm for the suppression of narrow-band interference in direct sequence spread spectrum (DSSS) systems, based on the open loop adaptive IIR notch filtering. The center frequency of the interference is monitored on-line by the adaptive lattice IIR notch filter in Cho and Lee (1993) or by time-frequency analysis in Amin (1997). The power of the interference signal is also estimated from the adaptive filters. Another lattice IIR notch filter is placed in front of the receiver, the notch of which is controlled by the frequency estimate to remove the interference. However, the IIR notch filter with the zeros on the unit circle also removes the information signal at the notch frequency while removing the interference and causes data distortion. Hence, the depth of the notch should also be adjusted for the trade-off between data distortion and effective interference reduction. The objective function for adjusting the depth of the notch is defined as the overall signal to noise ratio (SNR). The SNR is expressed as a function of filter parameters and the notch depth that maximizes the SNR is found. Simulation results show that the proposed algorithm yields better performance than the existing FIR notch filter and the conventional FIR LMS algorithm with very long taps.
Boosting image segmentation

This paper presents a new approach to image segmentation, based on the conditional random fields (CRF) modeling and AdaBoost. In the proposed segmentation algorithm, the discriminating characteristics are first learned online using a training machine, and then the learnt characteristics are used to improve the region segmentation. The proposed algorithm is devised to include any kind of features even if they have different semantics, and to learn the difference of regions by selecting and combining only a few discriminating features among them. These novel properties are accomplished by a new Gibbs energy derived from CRF, AdaBoost, and probabilistic interpretation of its strong classifier. Experimental results on various images show the effectiveness of the proposed method.
Co-saliency detection via seed propagation over the integrated graph with a cluster layer

This paper presents a method to detect common salient regions in a set of images. Since saliency and co-saliency detection are usually used as a pre-processing step for image processing and vision tasks, it is important to consider the complexity as well as the performance of an algorithm. Thus, we adhere to using low-level features and propose to detect co-salient objects with a new multilayer graph model in a bottom-up manner. Input images are represented by intra- and inter-image graphs composed of superpixels and their clusters, and each of these nodes obtains its initial co-saliency value from several cues. To generate resultant co-saliency values, foreground and background seeds are defined at parts of the unified multilayer graph, over which the seeds are propagated. Our experiments show that the proposed algorithm outperforms comparable methods on widely used public datasets, especially for the images that have various features.
High dynamic range (HDR) imaging by gradient domain fusion

This paper proposes a new HDR imaging method in the gradient domain based on the fusion of two images with different exposure. We first formulate an energy function for the binary labeling of each pixel based on the brightness and contrast measure of each image, which gives a binary map that determines whether the under-exposed image is better than the overly exposed one or not. Then a target gradient field is generated by combining the gradient fields of two images according to the binary map. Since the gradient field so combined is generally not integrable, we modify it via Poisson solver. This gradient field is considered that of HDR image and directly compressed to the display scale (in the gradient domain). Experiments show that the gradient domain fusion provides better results than the image domain methods, when fusing two differently exposed images.
Design of Multiplierless Lattice QMF: Structure and Algorithm Development

In this brief, efficient multiplierless design of lattice quadrature mirror filter bank is presented. Previous work by the authors has shown that splitting each lattice stage into cascade of subrotations results in larger stopband attenuation of filter than the conventional direct quantization. This brief extends the work further by exploiting the subrotations which yield more flexible sum of signed powers-of-two quantization. This enables us to find more possible discrete representations, and hence to reduce the quantization error. Also, an algorithm for the efficient gathering of candidate discrete coefficients is developed, based on the trellis-based searching approach. It substantially alleviates the overheads of optimization program, especially when the wordlengths and the number of nonzero digits are large. Several design examples are provided to show that the proposed structure with the candidate gathering algorithm provides improved frequency response.
Language-Independent Text-Line Extraction Algorithm for Handwritten Documents

Text-line extraction in handwritten documents is an important step for document image understanding, and a number of algorithms have been proposed to address this problem. However, most of them exploit features of specific languages and work only for a given language. In order to overcome this limitation, we develop a language-independent text-line extraction algorithm. Our method is based on connected components (CCs), however, unlike conventional methods, we analyze strokes and partition under-segmented CCs into normalized ones. Due to this normalization, the proposed method is able to estimate the states of CCs for a range of different languages and writing styles. From the estimated states, we build a cost function whose minimization yields text-lines. Experimental results show that the proposed method yields the state-of-the-art performance on Latin-based and Chinese script databases. Further, we submitted the proposed algorithm to the ICDAR 2013 handwriting segmentation competition and our method showed the best text-line extraction performance among 10 participant methods.
A multi-exposure image fusion algorithm without ghost effect

This paper proposes a new multi-exposure fusion algorithm for HDR imaging. The advantage of the fusion based method is that it does not actually generate HDR images so that the compression to LDR is also not needed. However, the conventional exposure fusion suffers from ghost effects when there are moving object or hand trembling because the output is just a weighted sum of multiple exposure input images. In order to alleviate this problem, we propose two kinds of weights in addition to the conventional weights that reflect the contrast, saturation and well-exposedness. The first is the weight that removes the pixels that can cause ghost effects, based on the observation that pixels near the boundary of moving objects often violate the rule that more highly exposed pixel have higher intensity level. The second is to reduce the weight when the correlation between an exposed image and the reference image is low. Experimental results show that the proposed method removes the ghost effectively, and yield less artifacts than the conventional methods.
Improved linear soft-input soft-output detection via soft feedback successive interference cancellation

We propose an improved minimum mean square error (MMSE) vertical Bell Labs layered space-time (V-BLAST) detection technique, called a soft input, soft output, and soft feedback (SIOF) V-BLAST detector, for turbo multi-input multioutput (turbo-MIMO) systems. We derive a symbol estimator by minimizing the power of the interference plus noise, given a priori probabilities of undetected layer symbols and a posteriori probabilities for past detected layer symbols. For a low-complexity implementation, an approximate SIOF algorithm is presented, which allows for a time-invariant realization of the symbol ordering and an MMSE filtering process. Another implementation, referred to as the iterative SIOF algorithm is introduced, which decides on symbol detection order based on a posteriori symbol probabilities to improve the detection performance. Simulations performed on a space-time bit-interleaved coded modulation (STBICM) architecture over quasi-static MIMO fading channels demonstrate that the SIOF V-BLAST detector provides performance gains over previous turbo-BLAST detectors, most notably when more transmit antennas are used.
Low-Complexity Decoding via Reduced Dimension Maximum-Likelihood Search

In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single best searching algorithm called sphere decoding, and 2) the recomputation of a set of weak symbols, i.e., those ignored in the reduced dimension search, for each strong symbol candidate found during the list tree search. Through simulations on M-quadrature amplitude modulation (QAM) transmission in frequency nonselective multi-input-multioutput (MIMO) channels, we demonstrate that the RD-MLS algorithm shows near constant complexity over a wide range of bit error rate (BER) (10-1 ~ 10-4), while limiting performance loss to within 1 dB from ML detection.
A convolution model and a cepstral filtering algorithm for the reduction of blocking artifacts

Proposes a convolution model and a cepstral filtering algorithm for reducing the blocking artifacts in compressed images and videos. Since each of the horizontal or vertical lines of the compressed image has abrupt changes at almost every 8th sample, images can be modeled as a convolution of the original sequence with an excitation signal of periodicity 8. Comparison of the cepstrums also shows that the multiples of 8th samples of the compressed signal's cepstrum usually have larger magnitudes than those of the original. The proposed filtering algorithm is to reduce the magnitudes of every 8th sample of the cepstrum, and disperse the reduced values to the neighboring samples. The algorithm is conducted in the cepstrum domain for reducing only the excitation signal which is the cause of blocking artifacts. Hence, the high frequency contents are not reduced by the postprocessing, whereas the conventional lowpass filtering blurs the image.
Reduction of ghost effect in exposure fusion by detecting the ghost pixels in saturated and non-saturated regions

This paper proposes a multiple exposure image fusion algorithm with reduced ghost. The basic idea is to adjust the weight map in the conventional fusion method in such a way that the ghost pixels are excluded. For this, pixels that cause ghost effect are detected in both of the saturated and non-saturated regions. In order to detect ghost pixels in the non-saturated region, we use the photometric relation and Gaussian mixture modeling (GMM) of a zero mean normalized cross correlation (ZNCC) map between a given exposure image and the reference. From this, we can also obtain static region where we construct an intensity mapping function (IMF) to detect the ghost pixels in saturated regions. Experimental results show that the proposed method generates high quality image without noticeable ghost effect, and yields less artifacts than the conventional methods.
Skin detection based on multi-seed propagation in a multi-layer graph for regional and color consistency

We propose a new skin detection method based on multi-seeds propagation in a multi-layer graph representation of an image. Initially, some of nodes in the graph are set to be foreground or background seeds based on a simple Bayesian skin detector, and they are propagated through the graph to find the skin probability in the manner of semi-supervised learning. The graph is designed to consider not only local and global coherence but also to consider the color consistency by constructing a multilayer graph of image and cluster layers. Extensive experiments on several datasets are conducted, which demonstrate that our method outperforms the existing methods in terms of various quantitative measures, such as accuracy, precision, recall and F-measure.
On the adaptive lattice notch filter for the detection of sinusoids

A lattice notch filter for the detection and enchancement of sinusoids in noise is presented, and the convergence properties of the algorithm are investigated using ODE (ordinary differential equation) analysis. It is shown that the frequency estimate of the algorithm for a single sinusoid is unbiased, regardless of the pole-zero contraction factor and the noise variance. An expression for the MSE (mean square error) of the frequency estimate and the output SNR of the algorithm is derived. Simulation results are presented to verify the theoretical analysis. Based on cascading the proposed second-order adaptive notch filter, three different approaches for the detection of multiple sinusoids are presented. The first approach uses a linear cascade structure, and the second and third approaches use a triangular cascade structure. The performance of the algorithms for multiple sinusoids is investigated, and computer simulation results are presented and discussed.
Design and VLSI implementation of CSD filter for pre-processing of image signals

In this paper, we investigate the design and VLSI implementation of CSD (canonic signed digit) coefficient filters for high speed digital filtering. First, CSD coefficient filters are designed, which can be used as format conversion filters in place of the ones employed for the MPEG2 TM5 (test model 5). It is shown that the proposed CSD filters perform better than the conventional one, while having lower hardware complexity. Second, the pipelined bit-serial and bit-parallel architectures for the CSD filter are proposed and verified through the VHDL simulation.
Embedded Panoramic Mosaic System Using Auto-Shot Interface

This paper proposes a panoramic image mosaic system for mobile devices. The proposed panoramic mosaic system is optimized for mobile devices by integer- programmable algorithms, and is user-friendly designed by 4- directional auto-shot interface. The proposed system consists of image acquisition by auto-shot interface, image transform onto cylindrical mosaic surface, luminance and color compensation, local alignment, image stitching, and blending. The images are automatically captured by the auto-shot interface which senses the user's camera motion and takes a picture when the camera motion is matched to the predefined motion model of images. The captured images are transformed and projected onto a cylindrical mosaic surface. Exposure difference is removed by color compensation, and the projected images are locally aligned by translational motion model. Then, the boundary of overlapped images are determined and synthesized by image stitching and blending. According to the experiments using real mobile devices, the proposed mosaic system shows good performance compared with other mosaic algorithms. Especially, since the proposed mosaic system operates on the usual mobile multimedia in real-time, the users can generate panoramic images easier than the personal computer based algorithms.
Principal subspace modification for multi-channel Wiener filter in multi-microphone noise reduction

In multi-microphone noise reduction for single desired speech signal, the principal subspace based multi-channel Wiener filter provides better performance compared with the conventional multi-channel Wiener filter. The principal subspace vector estimates the acoustic transfer function vector up to a scaling factor. However, as input SNR becomes lower, the error increases in the acoustic transfer function vector estimation. In this paper, we propose the principal subspace modification which is controlled by the angle between the principal subspace vector and the steering vector of the desired speech signal. In the simulation, the proposed method is evaluated with multi-channel speech data which are degraded by interfering noise coming from other direction. The simulation results show that the modification of principal subspace vector allows better performance compared to the conventional principal subspace based multichannel Wiener filter.
Stereo Matching using Multi-Directional Dynamic Programming and Edge Orientations

This paper proposes a stereo matching algorithm which employs an adaptive multi-directional dynamic programming (DP) scheme using edge orientations. A new energy function is defined in order to consider the discontinuity of disparity and occlusions, which is minimized by the multi-directional DP scheme. Chain codes are introduced to find the accurate edge orientations which provide the DP scheme with optimal multidirectional paths. The proposed algorithm eliminates the streaking problem of conventional DP based algorithms, and estimates more accurate disparity information in boundary areas. The experimental results using the Middlebury stereo images [7] demonstrate that our algorithm shows better performance than previous DP based approaches.
High Dynamic Range and Super-Resolution Imaging From a Single Image

This paper presents an algorithm for high dynamic range (HDR) and super-resolution (SR) imaging from a single image. First, we propose a new single image HDR imaging (HDRI) method based on the Retinex approach and exploit a recent single image SR method based on a convolutional neural network (CNN). Among many possible configurations of HDR and SR, we find an optimal system configuration and color manipulation strategy from the extensive experiments. Specifically, the best results are obtained when we first process the luminance component (<inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>) of input with our single image HDRI algorithm and then feed the enhanced HDR luminance to the CNN-based SR architecture that is trained by only luminance component. The ranges of chromatic components (<inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula>) are just scaled in proportion to the enhanced HDR luminance, and then they are bicubic interpolated or fed to the above CNN-based SR. Subjective and objective assessments for various experiments are presented to validate the effectiveness of the proposed HDR/SR imaging scheme.
Image registration algorithm based on regular sparse correspondences and SIFT

Image registration is to find the correspondences between different images of the same scene. It still remains a challenging problem especially when there is dramatic changes of object appearances. This paper presents a new image registration method for alleviating this problem, which first finds sparse correspondences and interpolates continuous dense motions from them. Unlike conventional registration methods, we regularly place control points to prevent biased distribution of feature points and find optimized correspondences by minimizing the cost function. The cost function is based on SIFT descriptors and considers the smoothness of motion and topological relations. Especially, the topological term prevents inconsistent solution like fold-over or duplication artifacts. For the optimization, we adopt a dual-layer belief propagation and coarse-to-fine scheme. Based on barycentric coordinates, we finally estimate dense motion from sparse correspondences. Experimental results show that the proposed method yields more plausible results and is computationally efficient.