nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic ROI based on K-means for remote photoplethysmography

Remote imaging photoplethysmography (RIPPG) can achieve contactless human vital signs monitoring. Though the remote operation mode brings a great convenience for RIPPG applications, the RIPPG signal quality is limited by the remote nature. Improving the RIPPG signal quality becomes an essential task in the clinical application of RIPPG. Since the region of interest (ROI) of the RIPPG transforms from a point to an area, there is a new approach to improving the RIPPG signal quality through refining the ROI. In this paper, we propose a dynamic ROI for RIPPG, which can automatically select the skin regions corresponding to good quality RIPPG signals. First, a fixed ROI is divided into non-overlapped blocks. Then two features are proposed to perform no-reference quality assessment for RIPPG signals from different blocks. After that, K-means clustering operates in a two dimensional feature space. A dynamic ROI can be selected for a video segment based on the clustering result, updated every two seconds. Nineteen healthy subjects were enrolled to test the proposed ROI selection method on both the facial region and the palmar region. Experimental results of heart rate measurement show that the proposed dynamic ROI method for RIPPG can effectively improve the RIPPG signal quality, compared with the state-of-the-art ROI methods for RIPPG.
Face liveness detection and recognition using shearlet based feature descriptors

Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by non-real faces such as a photograph or video of valid user. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to develop a multifunctional feature descriptor and an efficient framework which can be used to deal with both face liveness detection and recognition. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and softmax classifier are concatenated to detect face liveness and identify person. We evaluated this approach using CASIA Face Anti-Spoofing Database and the results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of this database, and is possible to significantly enhance the security of face recognition biometric system.
Merged-color histogram for color image retrieval

Conventional histogram-based image retrieval algorithms usually find only intersecting areas of the color-component distributions of images, and thus work well in matching images with exact colors instead of similar colors, especially for computer generated pictures. This could be greatly affected by overall variations, such as intensity changes. A novel merged-color histogram (MCH) method for color image retrieval is proposed to facilitate matching between similar colors by means of color quantization and palette merging. Color quantization compacts the color information and matches each color instead of color components, and matching of similar colors is accomplished using palette merging. Experimental results show that the proposed MCH method is about 11-32% more precise in the first 20 retrievals for the same image query, and is able to recall 14%-23% more relevant images in the first 100 retrievals, as compared to the conventional RGB-based histogram method.
Multi-subspace tree-structured vector quantizer design algorithms

To reduce further the searching complexity and memory requirement of tree-structured vector quantization (TSVQ), novel multisubspace TSVQ design algorithms and encoding techniques are proposed. The proposed multisubspace TSVQ design algorithms perform the vector quantization in the spatial domain while using specially designed subspace distortions in the transform domain as cost functions for the optimization process. The dimensionality and basis of subspace distortions are selected based on the local statistics of the partition associated with each nonterminal node of the tree. Experimental results show that extremely low subspace dimension can be used in multisubspace TSVQ based on a fixed transform domain to obtain a performance similar to that of the conventional TSVQ or single subspace TSVQ. The proposed generalized multisubspace TSVQ design algorithm is a general TSVQ design algorithm and can be utilized as a fast codebook design algorithm.<<ETX>>
Center-biased frame selection algorithms for fast multi-frame motion estimation in H.264

The new upcoming video coding standard, H.264, allows motion estimation performing on multiple reference frames. This new feature improves the prediction accuracy of inter-coding blocks significantly, but it is extremely computational intensive. Its reference software adopts a full search scheme. The complexity of multi-frame motion estimation increases linearly with the number of used reference frames. However, the distortion gain given by each reference frame varies with the motion content of the video sequence, and it is not efficient to search through all the candidate frames. In this paper, a novel center-biased frame selection method is proposed to speed up the multi-frame motion estimation process in H.264. We apply a center-biased frame selection path to identify the ultimate reference frame from all the candidates. Simulation results show that our proposed method can save about 77% computations constantly while keeping similar picture quality as compared to full search.
An SSIM-optimal H.264/AVC inter frame encoder

Rate-distortion optimization (RDO), in which distortion metric plays a vital role, has been proved to be an effective way in hybrid video coding. This paper proposes an improved rate-distortion optimization method based on SSIM (IRDO-SSIM) in RDO mode selection process. And the derivation of the proper multiplier to fit for the IRDO-SSIM is mainly described in this paper. Simulation results show that the proposed algorithm has better rate-distortion performance, especially for image sequences with middle-motion complexity or low encoding bit-rate as comparing with H.264/AVC using conventional RDO.
Low complexity preprocessing for 2D discrete multiwavelet transform

Multiwavelet transform can be implemented by tree-structured matrix filter banks which operate on vector coefficients instead of scalar ones. Therefore, preprocessing is usually required to extract vector input from the input signal for better performance. A 2D approximation preprocessing scheme for discrete multiwavelet transform is proposed in this paper. The multiwavelet system considered is the one constructed by Geronimo, Hardin and Massopust [1994] (GHM). Compared with 1D approximation-based method, the proposed preprocessing results in similar transform energy compression ability with additional advantage of lower computational complexity.
Embedded lossless wavelet-based image coding algorithm with successive partitioning and hybrid bit scanning

A new embedded lossless wavelet-based image coding algorithm called successive partition zero coder (SPZC) which use hybrid bit scanning is proposed. By successive partition the wavelet coefficients in the space-frequency domain and non-causal adaptive context modeling, SPZC outperforms other state-of-the-art coders such as SPIHT, CREW and LJPEG etc. in terms of coding efficiency even without zerotree analysis.
A Search Patterns Switching Algorithm for Block Motion Estimation

Center-biased fast motion estimation algorithms, e.g., block-based gradient descent search and diamond search, can perform much better than coarse-to-fine search algorithms, such as 2-D logarithmic search and three-step search. The latter type of algorithms, however, is more suitable for handling large motion content. To combine the advantages of both types of algorithms, an adaptive algorithm performing search patterns switching (SPS) is proposed in this paper. The proposed SPS algorithm classifies the motion content of a block using a simple yet efficient motion content classifier called error descent rate. Unlike other classifiers with heavy overhead, this classifier requires only the searching of a few points in the search window and then a division operation. Experimental results show that the proposed SPS algorithm is very robust.
A novel subtree partitioning algorithm for wavelet-based fractal image coding

A novel wavelet subtree partitioning algorithm is proposed, which divides a subtree into scalar quantized wavelet coefficients and a fractal coded sub-subtree. Based on this new technique, a variable size wavelet subtree fractal coding scheme for still image compression is developed. Experimental results show that the new scheme can achieve a nearly optimal partition of the wavelet subtree with substantially computational reduction as compared with Davis' (see Proc. SPIE Wavelet Applications II, Orlando, vol.2491, p.141-152, 1995) scheme.
Generalized partial distortion search algorithm for block-matching motion estimation

Quality against speed control for real-time video applications, such as low-bit-rate video conferencing or high quality video entertainment, is usually absent from many traditional fast block motion estimators. A novel block-matching algorithm for fast motion estimation, named generalized partial distortion search algorithm (GPDS), is proposed. It uses a halfway-stop technique with progressive partial distortion (PPD) to increase the chance of early rejection of impossible candidate motion vectors at very early stages. Simulations on PPD show 28 to 38 times computational reduction with only 0.45-0.50 dB PSNR performance degradation as compared to the full search algorithm. In addition, a new normalized partial distortion comparison method is proposed for enabling the control of searching speed against prediction accuracy by a quality factor k. This method also generalizes the conventional partial distortion search algorithm when k=1, and the normalized partial distortion search algorithm (NPDS) when k=/spl infin/. Experimental results show that GPDS with use of PPD could provide PSNR performance very close to the full search algorithm with 7 to 17 times speedup, and to NPDS with 22 to 33 times speedup, respectively, as compared to the full search algorithm.
Spatial coefficient partitioning for lossless wavelet image coding

In pyramidal wavelet representation, an image is decomposed into multiresolution and multifrequency subbands with sets of tree-structured coefficients, i.e. a spatial orientation tree which consists of coefficients at different resolutions and different orientations but associated with the same spatial location. The magnitudes of the coefficients in these trees measure the signal activity level of the corresponding spatial areas. A novel coefficient partitioning algorithm is introduced for splitting the coefficients into two sets using a spatial orientation tree data structure. By splitting the coefficients, the overall theoretical entropy is reduced due to the different probability distributions for the two coefficient sets. In the spatial domain, it is equivalent to identifying smooth regions of the image. A lossless coder based on this spatial coefficient partitioning has a better coding performance than other wavelet-based lossless image coders such as S + P and JPEG-2000.
No-reference image quality assessment using shearlet transform and stacked autoencoders

In this work, we describe an efficient generalpurpose no-reference (NR) image quality assessment (IQA) algorithm that is based on a new multiscale directional transform (shearlet transform) with a strong ability to localize distributed discontinuities. The algorithm relies on utilizing the sum of subband coefficient amplitudes (SSCA) as primary features to describe the behavior of natural images and distorted images. Then, stacked autoencoders are applied to exaggerate the discriminative parts of the primary features. Finally, by translating the NR-IQA problem into classification problem, the differences of evolved features are identified by softmax classifier. The resulting algorithm, which we name SESANIA (ShEarlet and Stacked Autoencoders based No-reference Image quality Assessment), is tested on several databases (LIVE, Multiply Distorted LIVE and TID2008) and shown to be suitable to many common distortions, consistent with subjective assessment and comparable to full-reference IQA methods and state-of-the-art general purpose NR-IQA algorithms.
A novel kite-cross-diamond search algorithm for fast video coding and videoconferencing applications

We propose a kite-cross-diamond search (KCDS) algorithm, which is an improved version of the well-known cross-diamond search (CDS) algorithm and small cross-diamond search (SCDS) algorithm. Unlike traditional search patterns in block matching algorithms, such as square, diamond or cross, which are all in vertical and horizontal symmetric shapes, the KCDS algorithm adopts a novel asymmetric kite-shaped search pattern to keep similar distortion while the speed of the motion estimation for stationary or quasi-stationary blocks is further boosted. Experimental results show that the KCDS algorithm could achieve 39% search point reduction as compared with CDS, whereas there is similar and even better prediction accuracy in low-motion sequences. Simulations show that KCDS is the fastest algorithm and it performs more accurately in some kinds of sequences. This algorithm is especially suitable for videoconferencing applications.
Dominant color image retrieval using merged histogram

A merged histogram method (MHM) is proposed for histogram-based image retrieval based on dominant colors in images. In MHM, colors from images and between images are merged to form their dominant color set, instead of color components. Images of similar color can thus be selected and ranked. Experimental results show that MHM can achieve up to 32% and up to 22% more precise than RGB-based histogram and perceptually weighted histogram methods, respectively.
Motion artifacts suppression for remote imaging photoplethysmography

Remote imaging photoplethysmography (RIPPG) is able to access human vital signs without physical contact. However most of the conventional RIPPG approaches are susceptive to motions of subjects or camera. Overcoming motion artifacts presents one of the most challenging problems. Focusing on the motion artifacts problem, the effects of motion artifacts on RIPPG signals were analyzed. In order to suppress motion artifacts for RIPPG, region of interest (ROI) is stabilized by using face tracking based on feature points tracking. And adaptive bandpass filter is further used to suppress the residual motion artifacts. With the addition of motion artifacts, the sorting of independent component analysis (ICA) outputs becomes more important, hence reference sine signals are generated to be correlated with ICA output components, and the cardiac pulse wave is automatically picked up from ICA output components, with the largest correlation coefficient. Fourteen subjects were enrolled to test the robustness with large motion artifacts for the proposed RIPPG method. Experimental results show that the proposed method could obtain a much better performance in accessing pulse rates for moving subjects, compared to the state-of-the-art method. The effectiveness of our method in motion artifacts suppression was verified by comparison with a commercial oximeter using Bland-Altman analysis and Pearson's correlation. With the efficient motion artifact suppression, RIPPG method has good potential in broadening the application of vital signs accesses.
A novel block motion estimation algorithm with controllable quality and searching speed

Quality control for real-time video applications, such as the very-low-bit-rate video conferencing or the high quality video entertainment, is usually absent from many traditional fast block motion estimators. A novel block-matching algorithm for fast motion estimation, named adjustable partial distortion search algorithm (APDS), is proposed. It is a new normalized partial distortion comparison method with adjustability on prediction accuracy against searching speed using a quality factor k. When k is set to 0, APDS could act as the normalized partial distortion search algorithm (NPDS). When k is set to 1, APDS perform as the conventional partial distortion search algorithm (PDS) and could give the best quality as obtained in the full search algorithm (FS). In addition, it uses a halfway-stop technique with progressive partial distortion (PPD) to increase the chance of rejection of impossible candidate motion vectors at very early stages. Simulations with PPD show that there is 24-62 times computational reduction with 0.31-0.83 dB PSNR performance degradation, as compared to the FS algorithm. Experimental results show that APDS could provide PSNR performance very close to the full search algorithm with speedup ratios of 18 times, and to NPDS with 32 times, respectively, as compared to the FS algorithm.
MPEG-7 dominant color descriptor based relevance feedback using merged palette histogram

This paper proposes techniques to improve the effectiveness of relevance feedback (RF) based image retrieval using MPEG-7 dominant color descriptor (DCD). The conventional RF query point moving techniques could not be directly applied on DCD due to the difference of the color spaces used in each DCD histogram. In order to tackle this problem, a new merged palette histogram (MPH) technique is proposed for generating a new query DCD histogram from the relevant image set. The new method has been implemented on a MPEG-7 XM with use of an image database containing 1000 images. The effectiveness of this new MPH-RF technique using different similarity measures is demonstrated by experimental results in the terms of averaged normalized modified retrieval rate (ANMRR) and visual retrieved image comparison.
Adaptive Quantization in DCT Domain for Distributed Video Coding

An improved quantization scheme with adaptive quantization levels (AQL) for transform-domain distributed video coding (DVC) is presented in this paper. In the proposed AQL, the quantization level for each DCT band is adaptively determined according to its importance measured by the magnitude of large coefficients. Experimental results demonstrate that the proposed AQL can achieve significant rate-distortion improvement as compared with uniform scalar quantization scheme with fixed quantization levels.
An improvement image vector quantization based on affine transformation

In this paper, the commonly used affine transformation (AT) in fractal coding technique is incorporated into image vector quantizer (VQ). Two new improved VQ coding schemes based on AT are investigated in detail without modifying the existing codebook. Experimental results show that the proposed method can obtain a better reproduced image as compared with the conventional VQ in terms of PSNR and visual quality even with a small size codebook. In addition, it does not need to generate an enlarged or specific codebook and has the advantage of low memory requirement.
Complexity reduced mode selection of H.264/AVC intra coding

One of the new features in the H.264/AVC encoder is the use of Lagrangian rate-distortion optimization (RDO) method during mode decision at the macroblock level. The RDO technique has been employed in H.264/AVC for intra prediction mode selection to achieve better coding efficiency. But the computation complexity of mode decision algorithm is extremely high. To reduce the complexity of mode decision, we propose an efficient and fast 4 times 4 intra prediction mode selection scheme. The proposed method reduces the candidate of the prediction modes based on the sum of absolute transformed difference (SATD) between the original block and the intra predicted block. Rank of each mode is obtained based on the SATD value. The candidate modes are further reduced by using the combination of rank and most probable mode. Simulation results demonstrate that the proposed mode decision method reduces about 95% of mode decision time of intra coding with ignorable degradation of coding performance.
Frame adaptive ROI for photoplethysmography signal extraction from fingertip video captured by smartphone

Photoplethysmography (PPG) has been widely used in clinical applications for monitoring vital signs especially heart rate by pulse oximeter. Recent researches have demonstrated the possibility of using fingertip video based PPG approach to estimate heart rate by smartphones. However, due to the variation of camera sensor characteristics in difference smartphones, the conventional fixed region-of-interest (ROI) for PPG signal extraction technique is not reliable. In this paper, a novel frame adaptive ROI method is proposed to detour the color saturation or cut-off distortion in the fingertip video capturing process for improving the reliability due to variation and limited dynamic range of the camera sensors in different smartphone models. Experimental results demonstrate that the proposed method can produce good pulsatile waveform and achieve high heart rate estimation accuracy using different smartphone models as compared with a FDA (U.S. Food and Drug Administration) approved commercial pulse oximeter.
A novel adaptive subspace distortion measurement technique for image vector quantization

An adaptive subspace distortion measurement technique is proposed to further reduce the complexity of the tree-structured vector quantization (TSVQ). The Euclidean distortion is adaptively replaced by dimensionally reduced transform-domain subspace distortions during tree codebook generation. To achieve good approximation of the Euclidean distortion, the dimensionality and basis of subspace distortions are selected on the basis of the local statistics of the partition associated with each nonterminal node of the tree. Thus, substantial reductions of both computational complexity and memory requirement can be obtained. Experimental results show that exceptionally low subspace dimension can be used in multisubspace TSVQ based on a fixed transform-domain to obtain a performance similar to that of conventional or single-subspace TSVQ.<<ETX>>
A hierarchical block matching algorithm using partial distortion measure

Most of the fast block matching algorithms reduce computation by matching only some of the locations inside the search area. In this paper, we propose a novel algorithm-hierarchical partial distortion search (HPDS)-which reduces the computational complexity of each distortion measure instead of the number of searching locations by using partial distortion measure. The partial distortion is obtained by calculating the distortion between the decimated block of current frame and that of previous frame. The motion vector is found hierarchically by searching the candidate motion vectors found from previous level of search. Each higher level uses partial distortion functions with decreased ratio of decimation. Experimental results show that the proposed algorithm can give 5 times speed up compared with full search while maintaining the MSE performance very close to full search. Moreover, the algorithm can achieve 12 times speed up when the MSE performance is kept close to that of three-step search. The proposed algorithm is suitable for applications such as HDTV and MPEG-2 where motion estimation accuracy is important. It is also suitable for real-time applications such as video conferencing and H.263 where encoding speed are critical.