nils-herrmann/clean_scirepeval huggingface dataset:
Wavelet Transform Based Variable Tree Size Fractal Video Coding

A new wavelet video compression scheme using adaptive fractal coding is presented in this paper. Through pyramidal wavelet transform, each video frame is decomposed into multiresolution subbands and then organized into a set of wavelet subtrees to represent motion activities. After multiresolution motion detection, these wavelet subtrees are class8ed into motion and non-motion subtress. The coding of non-motion subtrees is straighqoward and simple, while the motion subtress are adaptively separated and then encoded using variable tree size fractal coding. Experimental results show that the proposed scheme provides a superior pegormance in terms of PSNR as well as the subjective quality at low bit-rates.
A novel kite-cross-diamond search algorithm for fast block matching motion estimation

In order to fit the small cross-center-biased characteristic of the real world video sequences, an improved version of the well-known cross-diamond search algorithm (CDS) is proposed in this paper. Unlike traditional search pattern, such as square, diamond or cross - all are in vertically and horizontally symmetric shape, the kite-cross-diamond search (KCDS) algorithm adopts an asymmetric kite-shaped search patterns in the search patterns in the search step to keep similar distortion or even better in low-motion sequence while the speed of the motion estimation for stationary or quasi-stationary blocks in further boosted. Experimental results show that this KCDS algorithm could achieve 58% searching points reduction as compared with diamond search (DS) and 41% as compared with cross diamond search (CDS) whereas similar prediction accuracy is preserved. Simulations show that KCDS is particularly faster and more accurate in some kinds of sequences. This algorithm is especially suitable for videoconferencing applications.
Normalized partial distortion search algorithm for block motion estimation

Many fast block-matching algorithms reduce computations by limiting the number of checking points. They can achieve high computation reduction, but often result in relatively higher matching error compared with the full-search algorithm. A novel fast block-matching algorithm named normalized partial distortion search is proposed. The proposed algorithm reduces computations by using a halfway-stop technique in the calculation of the block distortion measure. In order to increase the probability of early rejection of non-possible candidate motion vectors, the proposed algorithm normalized the accumulated partial distortion and the current minimum distortion before comparison. Experimental results show that the proposed algorithm can maintain its mean square error performance very close to the full-search algorithm while achieving an average computation reduction of 12-13 times, with respect to the full-search algorithm.
A novel histogram-biasing factor for fast sorted histogram-based measurement in large image database retrieval system

The exhaustive histogram matching is usually the most computationally intensive part for any query in most large image database retrieval systems. In this paper, we introduce a histogram-biasing factor (HBF) to measure the biased-behavior of ordered-bins in a sorted histogram. The proposed HBF can be used to increase the early rejection rate of unreliable or impossible candidate reference images based on one of the sorted histograms. Moreover, it can be treated as a color-histogram descriptor. Only images with very closed HBF are taken into account, searching speed can thus be increased without loss of accuracy. Experimental results show that the proposed factor results in up to 13 times speedup meanwhile providing the exhaustive retrieval performance.
A novel weighted cross prediction for H.264 intra coding

In this paper, a novel weighted cross prediction (WCP) mode is proposed to replace DC mode in Intra_4×4 prediction of H.264/AVC. In the proposed scheme, the upper right part of one 4×4 block mainly employs vertical prediction while the lower left part mainly uses horizontal prediction, predicting both in vertical and horizontal directions in one block. This scheme uses simple prediction equations with fixed weighting coefficients. Experimental results show that WCP has improvement compared to H.264 and it is very competitive while comparing to other Intra_4×4 prediction algorithms.
A fast H.264 intra prediction algorithm using macroblock properties

In the newest video coding standard of H.264, intra frame prediction is conducted in the spatial domain. There are two types of intra prediction for luma - Intra/spl I.bar/16/spl times/16 does prediction for the whole 16/spl times/16 luma macroblock and Intra/spl I.bar/4/spl times/4 does prediction for 4/spl times/4 block. Four and nine modes are supported by Intra/spl I.bar/16/spl times/16 and Intra/spl I.bar/4/spl times/4, respectively. The full search algorithm is used in the JVT reference software to choose the best modes, however it is very computationally expensive. In this paper, a new fast intra prediction algorithm using macroblock properties (FIPAMP) is proposed. Experimental results show that the proposed fast algorithm can achieve 10% to 40% computation reduction while maintaining similar PSNR and bit rate performance of H.264 codes.
Lossless wavelet coder with adaptive orientational prediction

This paper proposes a prediction-based lossless wavelet coder, which is adaptive to the orientation of the wavelet pyramid. We adapt the joint statistical regularities in the wavelet domain to create the predictor adaptively, thus eliminating the need to transmit the predictor and reducing the coding overhead. For simplicity, we adopt a simple adaptive predictor. Numerical results show that our coder is superior to the well-known JPEG lossless standard.
Adaptive block truncation filter for MVC depth image enhancement

In Multiview Video plus Depth (MVD) format, virtual views are generated from decoded texture videos with decoded depth images through Depth Image based Rendering (DIBR). 3DV-ATM is a reference model for H.264/AVC based Multiview Video Coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding of 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized views. This paper presents a low complexity adaptive block truncation filter to recover the sharp object boundaries of depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results show that sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views.
A novel inverted index file based searching strategy for video copy detection

The demand of video copy detection system is growing rapidly, as the development of online video uploading and sharing. In the past two decades, the research concentrates on the video information extraction or feature building rather than the fast searching strategy. However, as the number of videos is growing, fast searching in video copy detection systems has become a big issue. In this paper, we propose a novel fast searching strategy for Inverted Index File (IIF) based video copy detection system by using fingerprinting technology. The proposed searching approach consists of two parts — fingerprint matching and video fragment matching. To speed up the fingerprint matching process, a table lookup operation is utilized that rely on the counting of matched sub-fingerprints instead of Hamming Distance metric. For video fragment matching, all fingerprint candidate is used to propose more than one matched video candidate with a different similarity score. The proposed fast searching strategy is tested on experimental content-based video copy detection system with different fingerprinting methods, distortion types and video database scale. Experimental results show that the proposed searching approach achieve high accuracy, and is around 10 times faster compared with the conventional IIF method. Moreover, with database upscaling, the searching rate of the proposed approach is faster than the conventional IIF methods that further make it a potential candidate to be used in large-scale video copy detection systems.
No-Reference Video Quality Assessment With 3D Shearlet Transform and Convolutional Neural Networks

In this paper, we propose an efficient general-purpose no-reference (NR) video quality assessment (VQA) framework that is based on 3D shearlet transform and convolutional neural network (CNN). Taking video blocks as input, simple and efficient primary spatiotemporal features are extracted by 3D shearlet transform, which are capable of capturing natural scene statistics properties. Then, CNN and logistic regression are concatenated to exaggerate the discriminative parts of the primary features and predict a perceptual quality score. The resulting algorithm, which we name shearlet- and CNN-based NR VQA (SACONVA), is tested on well-known VQA databases of Laboratory for Image & Video Engineering, Image & Video Processing Laboratory, and CSIQ. The testing results have demonstrated that SACONVA performs well in predicting video quality and is competitive with current state-of-the-art full-reference VQA methods and general-purpose NR-VQA algorithms. Besides, SACONVA is extended to classify different video distortion types in these three databases and achieves excellent classification accuracy. In addition, we also demonstrate that SACONVA can be directly applied in real applications such as blind video denoising.
Spatial coefficient partitioning for lossless wavelet image coding

A novel coefficient partitioning algorithm is introduced for splitting the coefficients into two sets using spatial orientation tree data structure. By splitting the coefficients, the overall theoretical entropy is reduced due to the different probability distribution for the two coefficient sets. In spatial domain, it is equivalent to identifying smooth regions of the image. A lossless coder based on this spatial coefficient partitioning is described. Experimental results show that the new algorithm has a better coding performance than other wavelet based lossless image coder such as S+P and JPEG-2000.
A novel early-accepted partial distortion algorithm for fast block motion estimation

The speed improvement of partial distortion search algorithms is always limited by the partition size taken for comparison. By normalizing the comparison between the accumulated partial distortion and the current minimum distortion, the normalized partial distortion search (NPDS) algorithm can virtually maximize the speed-up ratio under this theoretical limit. A novel early acceptance (EA) technique is first proposed to achieve further speed-up. Unlike most conventional algorithms that only reject impossible candidate motion vectors, our algorithm is capable of accepting potential minimum candidates at a very early stage. Simulation result shows that our EA technique can increase the speed-up ratio of NPDS up to 5.46 times more with only 0.04 dB PSNR degradation. With our new quality control at q=0.5, our early-accepted partial distortion search (EAPDS) algorithm provides a speed-up ratio of around 28.45-36.60 times while maintaining a small amount of PSNR degradation from 0.18-0.31 dB as compared to full search (FS). Experiments also prove that our algorithm out-performs NPDS in both speed and accuracy.
Fast block-matching motion estimation by recent-biased search for multiple reference frames

Multi-frame motion compensation improves the rate-distortion performance substantially but introduces much higher loading to the system. Without considering temporal correlations, conventional single-frame block-matching algorithms can be used to search multiple frames in a rather inefficient frame-by-frame way. In order to exploit the motion characteristic in long-term memory, a multi-frame extension of the well-known cross-diamond search algorithm is proposed. Unlike those algorithms that evenly search each reference frame, our algorithm adopts a novel recent-biased spiral-cross search pattern to sub-sample the 3-dimensional memory space as a whole. This approach significantly boosts the efficiency of the block-matching process. Two new techniques, stationary block tracking and multiple searching paths, are employed to further improve the speed and accuracy. As compared to full search, experimental results show that our algorithm can reduce up to 99.5% complexity in terms of searching points while limiting the PSNR loss in 0.04 dB. Simulations also prove that our algorithm out-performs the cross-diamond search and diamond search algorithms in speed and accuracy.
Subsampled Block-Matching for Zoom Motion Compensated Prediction

Motion compensated prediction plays a vital role in achieving enormous video compression efficiency in advanced video coding standards. Most practical motion compensated prediction techniques implicitly assume pure translational motions in the video contents for effective operation. Some attempts aiming at more general motion models are usually too complex requiring parameter estimation in practical implementation. In this paper, zoom motion compensation is investigated to extend the assumed model to support both zoom and translation motions. To accomplish practical complexity, a novel and efficient subsampled block-matching zoom motion estimation technique is proposed which makes use of the interpolated reference frames for subpixel motion estimation in a conventional hybrid video coding structure. Specially designed subsampling patterns in block matching are used to realize the translation and zoom motion estimation and compensation. No zoom parameters estimation and additional frame buffers are required in the encoder implementation. The complexity of the decoder is similar to the conventional hybrid video codec that supports subpixel motion compensation. The overall increase in memory requirement and computational complexity is moderate. Experimental results show that the new technique can achieve up to 9.89% bitrate reduction using KTA2.2r1 reference software implementation.
Distance-based weighted prediction for Adaptive Intra Mode Bit Skip in H.264/AVC

Adaptive Intra Mode Bit Skip (AIMBS) technique using boundary pixels smoothness has been shown to achieve coding efficiency improvement for H.264/AVC's Intra_4x4 coding in relatively large QPs. However, the DC mode in the Multiple-Prediction of the AIMBS becomes much less effective. To tackle this problem and further improve the coding efficiency, distance-based weighted prediction (DWP) is proposed to replace DC mode in Multiple-Prediction for predicting blocks without directional preferences. The proposed method is named as AIMBS-DWP that can enhance the robustness of AIMBS in much larger range of QPs and achieve higher rate-distortion performance. Experimental results show that an average bitrate reduction of 3.79% with lower computational requirement can be obtained by AIMBS-DWP as compared with H.264/AVC. The improvement is especially obvious in high visual quality configurations with small QPs.
Watershed and Random Walks based depth estimation for semi-automatic 2D to 3D image conversion

Depth map estimation from a single image is the key problem for the 2D to 3D image conversion. Many 2D to 3D converting processes, either automatic or semi-automatic, are proposed before. Quality of the depth map from automatic methods is low and there exists wrong depth values due to errors estimation in depth cue extraction. The semi-automatic approaches can generate a better quality of depth map based on the user-defined labels, which indicate a rough estimation of depth values in the scene, to generate the rest of depth value and reconstruct the stereoscopic image. However, they require complexity system and are very computational intensive. A simplified approach is to combine the depth maps from Graph Cuts and Random Walks to persevering the sharp boundary and fine detail inside the objects. The drawback is the time consuming of the energy minimization in the Graph Cuts. In this paper, a fast Watershed segmentation based on the priority queue, which indicates the neighbor distance relationship, is used to replace the Graph Cuts to generate the hard constraints depth map. It is appended to the neighbor cost in the Random Walks to generate the final depth map with hard constraints in the objects boundaries regions and fine detail inside objects. The Watershed and Random Walks are low computational intensive and can achieve approximate real time estimation which results in a fast stereoscopic conversion process. Experimental results demonstrate that it can produce good quality stereoscopic image in very short time.
Adaptive motion tracking block matching algorithms for video coding

In most block-based video coding systems, the fast block matching algorithms (BMAs) use the origin as the initial search center, which may not track the motion very well. To improve the accuracy of the fast BMAs, a new adaptive motion tracking search algorithm is proposed. Based on the spatial correlation of motion blocks, a predicted starting search point, which reflects the motion trend of the current block, is adaptively chosen. This predicted search center is found closer to the global minimum, and thus the center-biased BMAs can be used to find the motion vector more efficiently. Experimental results show that the proposed algorithm enhances the accuracy of the fast center-biased BMAs, such as the new three-step search, the four-step search, and the block-based gradient descent search, as well as reduces their computational requirement.
Dominant Color Structure Descriptor for Image Retrieval

A new dominant color structure descriptor (DCSD) is proposed in this paper. It is designed to provide an efficient way to represent both color and spatial structure information with single compact descriptor. The descriptor combines the compactness of dominant color descriptor (DCD) and the retrieval accuracy of color structure descriptor (CSD) to enhance the retrieval performance in a highly efficient manner. The feature extraction and similarity measure of the descriptor are designed to address the problems of the existing descriptors while utilize the advantages of them. Experimental results show that DCSD has a significant improvement on both retrieval performance and descriptor size over DCD. An eight-color DCSD (DCSD 8) gives an averaged normalized modified retrieval rate (ANMRR) of 0.0993 using MPEG-7 common color dataset, outperforming compact configurations of scalable color descriptor and color structure descriptor with smaller descriptor size.
A novel four-step search algorithm for fast block motion estimation

Based on the real world image sequence's characteristic of center-biased motion vector distribution, a new four-step search (4SS) algorithm with center-biased checking point pattern for fast block motion estimation is proposed in this paper. A halfway-stop technique is employed in the new algorithm with searching steps of 2 to 4 and the total number of checking points is varied from 17 to 27. Simulation results show that the proposed 4SS performs better than the well-known three-step search and has similar performance to the new three-step search (N3SS) in terms of motion compensation errors. In addition, the 4SS also reduces the worst-case computational requirement from 33 to 27 search points and the average computational requirement from 21 to 19 search points, as compared with N3SS.
Motion-Resistant Remote Imaging Photoplethysmography Based on the Optical Properties of Skin

Remote imaging photoplethysmography (RIPPG) can achieve contactless monitoring of human vital signs. However, the robustness to a subject's motion is a challenging problem for RIPPG, especially in facial video-based RIPPG. The RIPPG signal originates from the radiant intensity variation of human skin with pulses of blood and motions can modulate the radiant intensity of the skin. Based on the optical properties of human skin, we build an optical RIPPG signal model in which the origins of the RIPPG signal and motion artifacts can be clearly described. The region of interest (ROI) of the skin is regarded as a Lambertian radiator and the effect of ROI tracking is analyzed from the perspective of radiometry. By considering a digital color camera as a simple spectrometer, we propose an adaptive color difference operation between the green and red channels to reduce motion artifacts. Based on the spectral characteristics of photoplethysmography signals, we propose an adaptive bandpass filter to remove residual motion artifacts of RIPPG. We also combine ROI selection on the subject's cheeks with speeded-up robust features points tracking to improve the RIPPG signal quality. Experimental results show that the proposed RIPPG can obtain greatly improved performance in accessing heart rates in moving subjects, compared with the state-of-the-art facial video-based RIPPG methods.
A new multidirectional extrapolation hole-filling method for Depth-Image-Based Rendering

Depth-Image-Based Rendering (DIBR) is widely used to generate virtual view of a scene from a known view with associated depth map in 3D video applications. However, disocclusion arises in image warping of DIBR. Many hole-filling methods have been proposed such as constant color, horizontal interpolation, horizontal extrapolation, and variational inpainting, but they cause different types of annoying artifact for large holes with complex texture background. In this paper, a novel multidirectional extrapolation hole-filling method is proposed to enhance visual quality for large hole-filling with complex texture background. The proposed method uses neighbor pixels' texture features to estimate hole-filling direction in a pixel-by-pixel manner. Experimental results demonstrated that the proposed method could provide better visual quality compared with conventional methods for virtual views synthesis with high-quality depth map.
Enhanced SAITD Cost Function for H.264/AVC Intra 4×4 Mode Decision

In this paper, an enhanced sum of absolute integer transform difference (ESAITD) cost function with rate predictor "sum of non-zero coefficient frequencies" from CAVLC-based rate estimation is proposed for Intra_4x4 mode decision in H.264/AVC. This rate predictor is using simple but significant parameter to approximate the total encoded bitrate accurately. Experimental results show that ESAITD achieves much better rate-distortion (RD) performance than sum of absolute Hadamard transform difference (SATD) cost function. It also outperforms sum of absolute integer transform difference (SAITD) cost function in medium and high bitrate. Overall, ESAITD can encode the sequences with high quality.