nils-herrmann/clean_scirepeval huggingface dataset:
Image compression for facial photographs based on wavelet transform

This paper presents a wavelet based image compression algorithm specifically tailored for facial photographs. The proposed method dramatically reduces memory requirements for facial image databases. The algorithm makes it possible to reach compression rates of 0.25 to 0.1 bpp without compromising the visual quality of the facial features used for identification purposes. This is made possible by utilizing a novel strategy for quantizing the wavelet coefficients, where the spatial content and the frequency distribution of each input image are combined to produce a quantization scheme which is spatially and frequency dependent and different for each image being processed. The main steps of the algorithm are as follows: detection of more and less important spacial areas in the photograph; discrete wavelet transform; space and frequency dependent quantization customized for a specific frequency distribution of each image; and entropy encoding.
A human visual system-based 3D video quality metric

Although several 2D quality metrics have been proposed for images and videos, in the case of 3D efforts are only at the initial stages. In this paper, we propose a new full-reference quality metric for 3D content. Our method is modeled around the HVS, fusing the information of both left and right channels, considering color components, the cyclopean views of the two videos and disparity. Performance evaluations showed that our 3D quality metric successfully monitors the degradation of quality caused by several representative types of distortion and it has 86% correlation with the results of subjective evaluations.
An H.264-based scheme for 2D to 3D video conversion

An efficient 2D to 3D video conversion method which utilizes the H.264 motion estimation process is proposed. The relationship between motion and depth was approximated via our non-linear scheme, that is based on the characteristics of 3D visual perception. Performance evaluations show that our approach outperforms other existing motion-based depth map estimation technique by 1.84 dB PSNR via providing more realistic depth information for the scene. Subjective comparisons also confirm the efficiency of our method.
Demystifying High-Dynamic-Range Technology: A new evolution in digital media.

High-dynamic-range (HDR) technology has attracted a lot of attention recently, especially in commercial trade shows such as the Consumer Electronics Show, the National Association of Broadcasters Show, the International Broadcasting Convention, and Internationale Funkausstellung Berlin. However, a great deal of mystery still surrounds this new evolution in digital media. In a nutshell, HDR technology aims at capturing, distributing, and displaying a range of luminance and color values that better correspond to what the human eye can perceive. Here, the term luminance stands for the photometric quantity of light arriving at the human eye measured in candela per square meter or nits. The color refers to all the weighted combinations of spectral wavelengths, expressed in nanometers (nm), emitted by the sun that are visible by the human eye . The human eye can perceive a dynamic range of over 14 orders of magnitude (i.e., the difference in powers of ten between highest and lowest luminance value) in the real world through adaptation. However, at a single adaptation time, the human eye can only resolve up to five orders of magnitude, as illustrated in Figure 2. Dynamic range denotes the ratio between the highest and lowest luminance value. As reported in Table 1, there are different interpretations for dynamic range, depending on the application. For instance, in photography, dynamic range is measured in terms of f-stops, which correspond to the number of times that the light intensity can be doubled.
A hybrid coding method for digital HDTV signals

Digitally transmitted video images suffer from sudden degradation in picture quality at high channel noise levels. This is due to the variable length coding and the large synchronization blocks used. To remedy that, we propose encoding the DCT coefficients by a hybrid fixed and variable lengths compression scheme. This method improves the compression ratio by approximately 20% and increases the system's resistance to channel errors. We then combine this hybrid algorithm with an error protected synchronization method which uses small blocks of size 32/spl times/16 pixels. The resulting combined schemes 1) significantly improve the error resistance characteristic of the system, 2) eliminate the abrupt picture degradation, and 3) does not alter the original data transmission rate.
Optimized contrast reduction for crosstalk cancellation in 3D displays

Subtractive crosstalk cancelation is an effective way to reduce the appearance of ghosting in 3D displays. However, effective cancelation requires the black level of the input images to be raised above zero, which reduces the image contrast and visual quality. Previous methods for selecting the raised black level do not consider the image content; they are either based on the worst case or they do not guarantee complete crosstalk cancelation. Previous methods also scale the red, green and blue channels independently, which results in images with washed out colors. This paper provides two contributions; first we derive the minimum amount that the black level has to be raised when using linear scaling in RGB space to ensure crosstalk can be fully cancelled out for a particular image. Second we propose that instead of scaling the images in RGB space, to scale the luma channel in YCbCr color space while keeping the chroma values constant to better preserve color. We also derive the minimum amount that the luma range has to be compressed to ensure that crosstalk can be fully canceled out. Experimental results show that our methods produce images with better color and contrast compared to scaling the RGB channels based on the worst case, while still guaranteeing crosstalk can be fully canceled out.
Content adaptive complexity reduction scheme for quality/fidelity scalable HEVC

There has been significant interest in developing a scalable version of the High Efficiency Video Coding (HEVC) standard. As expected, the HEVC scalable video version increases the complexity of the codec compared to the non-scalable counterpart. In this paper, we propose an adaptive early-termination interlayer motion prediction mode search that significantly reduces HEVC/SVC's coding complexity by up to 85.77%, while maintaining the overall bitrate.
DFVisor: Scalable network virtualization for QoS management in cloud computing

Increasingly cloud-based virtual networking environments are required to provide fine-grained Quality of Service (QoS) management without sacrificing scalability. However, no single approach currently can meet these requirements simultaneously. This paper introduces a layered concept that uses a common overlay mechanism to virtualize networks and enable fine-grained QoS management through resource slicing. Based on this mechanism, a fully distributed network virtualization platform called Distributed FlowVisor (DFVisor) is proposed. It uses a layered overlay to improve the network addressing space, reduce flow setup latency, and remove the single point of failure in the network - the central slice controller. Through a distributed synchronized two-level database, DFVisor incorporates a push-based flow setup and statistics collecting mechanism using a dedicated data channel to address the scalability issues caused by the current pull-based mechanism and the limited control channel bandwidth shared by both control flows and network statistics. The potential issues in DFVisor implementation and evaluation are discussed for future research.
Applying Knowledge of Users with Similar Preference to Construct Surrogate Models of IGA

Interactive genetic algorithms (IGAs) are effective methods of solving optimization problems with qualitative indices. The problem of user fatigue resulting from the user’s evaluations has a negative influence on the performance of these algorithms. Employing various surrogate models to evaluate (a part of ) individuals instead of a user is a feasible approach to solve the problem. Previous studies have not fully utilized knowledge provided by users with a similar preference when constructing these models. The problem of constructing surrogate models by using the knowledge of users with a similar preference was focused in this study. Users with a similar preference participating in the evolution were identified by using the collaborative filtering algorithm based on the nearest neighbor, and the individuals evaluated by these users were chosen as a part of samples for training the surrogate model of the current user’s cognition. The proposed method was applied to an evolutionary fashion design system, and the experimental results showed that the proposed method can improve the capability in exploration on the premise of greatly alleviating user fatigue.
Lossless compression of JPEG coded photo albums

The explosion in digital photography poses a significant challenge when it comes to photo storage for both personal devices and the Internet. In this paper, we propose a novel lossless compression method to further reduce the storage size of a set of JPEG coded correlated images. In this method, we propose jointly removing the inter-image redundancy in the feature, spatial, and frequency domains. For each album, we first organize the images into a pseudo video by minimizing the global predictive cost in the feature domain. We then introduce a disparity compensation method to enhance the spatial correlation between images. Finally, the redundancy between the compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Moreover, our proposed scheme is able to losslessly recover not only raw images but also JPEG files. Experimental results demonstrate the efficiency of our proposed lossless compression, which achieves more than 12% bit-saving on average compared with JPEG coded albums.
Interactive genetic algorithm assisted with collective intelligence from group decision making

Interactive genetic algorithms (IGAs) have been successfully applied to optimize problems with aesthetic criteria by embedding the intelligent evaluations of a user into the evolutionary process. User fatigue caused by frequent interactions, however, often greatly impairs the potentials of IGAs on solving complicated optimization problems. Taking the benefits of collective intelligence into account, we here present an IGA with collective intelligence which is derived from a mechanism of group decision making. An IGA with interval individual fitness is focused here and it can be separately conducted by multiple users at the same time. The collective intelligence of all participated users, represented with social and individual knowledge, is first collected by using a modified group decision making method. Then the strategy of applying the collective intelligence to initialize and guide the single evolution of the IGA is given. With such a multi-user promoted IGA framework, the performance of a single IGA is expected to be evidently improved. In a local network environment, the algorithm is applied to a fashion design system and the results empirically demonstrate that the algorithm can not only alleviate user fatigue but also increase the opportunities of IGAs on finding most satisfactory solutions.
Example-Based Super-Resolution With Soft Information and Decision

The one-to-one correspondence between co-occurrence image patches of two different resolutions is extensively used in example-based super-resolution (SR). Due to the dimensionality gap between low resolution (LR) and high resolution (HR) spaces, however, an LR patch may correspond to a number of HR patches in practice. This ambiguity is difficult to be overcome with examples representing a deterministic mapping. In this paper, we propose a statistical method for exploiting the one-to-many correspondence between LR and HR patches, which we call soft information and decision. Soft information means an LR patch is mapped to a pixel-wise distribution of all its possible HR counterparts, rather than a single or a limited set of HR candidates. Relying on the soft information, example-based SR is then regarded as an optimization problem to best preserve the local consistency in the recovered HR image. This problem is solved with an efficient message passing algorithm with a factor graph model. The final decision on the HR pixel value is made upon the maximum a posteriori estimation and is called a soft decision. Experimental results demonstrate the superiority of the proposed method compared with the state-of-the-art methods, in terms of both the subjective and objective quality of synthesized HR images.
An improved expanding window fountain codes for unequal error protection

An improved expanding window fountain code for unequal error protection is proposed in this paper. For expanding window fountain (EWF) codes, the increased performance of more important bits (MIB) is achieved at the cost of decreased performance of less important bits (LIB). On the basis of EWF codes, the proposed method uses the expanding window to cover all the input information symbols and alter the selection of symbols incorporated in the output symbols. Simulation results show that the improved EWF codes reduce the difference between MIB and LIB set in bit error rate (BER). And the performance of the proposed fountain codes excels EWF codes.
Fast H.264/MPEG-4 AVC Transcoding Using Power-Spectrum Based Rate-Distortion Optimization

Since variable block-size motion compensation (MC) and rate-distortion optimization (RDO) techniques are adopted in H.264/MPEG-4 AVC, modes and motion vectors (MVs) in input stream can no longer be reused equivalently efficient over a wide range of bit rate in transcoded streams. This paper proposes a new RDO model to maintain good coding efficiency and greatly reduce computation of the H.264/MPEG-4 AVC transcoding, in which the distortion caused by motion and mode changes is not calculated directly from the sum of absolute difference (SAD) or the sum of square difference (SSD) between source signals and interpolated prediction signals. Instead, distortion is directly estimated from MV variation and the power spectrum (PS) of the prediction signal generated from input stream. The proposed RDO model can be applied to both the pixel-domain transcoding and the transform-domain transcoding even when coded signals are not reconstructed at all. Furthermore, the techniques as to derive the Lagrangian multiplier in the proposed model are developed in respective pixel- and transform-domains. Additionally, we propose an H.264/MPEG-4 transcoding scheme that demonstrates the advantage of the proposed RDO model in terms of peak signal-to-noise ratio and transcoding speed, in which P-pictures are transcoded in the pixel domain for achieving reconstructed high quality and B-pictures are transcoded in the transform domain for high-transcoding speed.
Sensitivity Improvement of SAW NO2 Sensors by p-n Heterojunction Nanocomposite Based on MWNTs Skeleton

As one of the major air pollutants, nitrogen dioxide (NO2) is seriously harmful to humans and the environment. The development of surface acoustic wave (SAW) sensors provides great potential for manufacturing miniaturized NO2 sensor with high sensitivity, and high stability. In this paper, a SAW NO2 sensor with a sensing layer formed by nanocomposite materials mixed with multi-walled carbon nanotubes (MWNTs) is presented. The SAW device used in this paper is a commercialized two-port Rayleigh wave resonator and the main sensitive material to NO2 is phthalocyanine copper (CuPc). In order to increase the sensitivity of CuPc to NO2 at relative low temperature, MWNTs and metal-oxide nanoparticles are doped into the sensitive films. The p-n heterojunctions formed between the metal-oxide-semiconductor and the CuPc/MWNTs sensitive materials improve the performance of the sensor at low temperature down to 50 °C. The sensor shows good linearity and repeatability to NO2 with bulk concentration of 1-80 ppm, and the sensitivity is 50.5 Hz/ppm at 50 °C.
Set-based genetic algorithms for solving many-objective optimization problems

Many-objective optimization problems are very common and important in real-world applications, and there exist few methods suitable for them. Therefore, many-objective optimization problems are focused on in this study, and a set-based genetic algorithm is presented to effectively solve them. First, each objective of the original optimization problem is transformed into a desirability function according to the preferred region defined by the decision-maker. Thereafter, the transformed problem is further converted to a bi-objective optimization one by taking hyper-volume and the decision-maker's satisfaction as the new objectives, and a set of solutions of the original optimization problem as the new decision variable. To tackle the converted bi-objective optimization problem by using genetic algorithms, the crossover operator inside a set is designed based on the simplex method by using solutions of the original optimization problem, and the crossover operator between sets is developed by using the entropy of sets. In addition, the mutation operator of a set is presented to obey the Gaussian distribution and change along with the decision-maker's preferences. The proposed method is applied to five benchmark many-objective optimization problems, and compared with other six methods. The experimental results empirically demonstrate its effectiveness.
Off-Line Motion Description for Fast Video Stream Generation in MPEG-4 AVC/H.264

The rate-distortion optimal mode decision as well as motion estimation adopted in H.264 brings a big challenge to real-time encoding and transcoding due to the high computation complexity. In this paper, we propose a hierarchical motion description model to present the motion data of each macroblock (MB) from coarsely to finely. A preprocessing approach is developed to estimate the motion data for each MB at each quality level with regard to its reference quality, its adjacent MBs and the target bit-rate. The resulting motion data can be coded and stored as metadata in a media file or a stream. Moreover, we propose a method to readily extract the specific motion data from the model for each MB at given bit-rates. Experimental results have shown the effectiveness of our proposed motion description model in terms of coding efficiency as well as fast bit-rate adaptation in comparison with that of H.264
Internal-video mode dependent directional transform

As the projection of the real world, videos usually have many repeated patterns with similar structures cross regions, presenting strong non-local correlations. Moreover, different videos own different characteristics. Exploitation of the non-local correlations by off-line training of transforms has attracted considerable attention over the past years for compression. However, the samples used for training the transforms are usually collected from a predefined set of training videos to avoid the transmission of transform matrixes. There is no guarantee that the characteristics of those training videos and the corresponding transforms fit the current coding video very well. To address that, this paper proposes an internal-video mode dependent directional transform in HEVC for intra coding. In this scheme, for coding a video clip, based on different directions, a set of sample blocks is collected for each direction to train a Karhunen-Loeve transform at the video clip level. During encoding, the better transform between the proposed transform and original DCT/DST in HEVC is determined based on rate-distortion optimization. These transform matrixes are entropy coded to the bitstream. The proposed method can capture the statistical characteristics of the coded video clip and provide efficient transforms. Experimental results show that the proposed method achieves significant performance improvements in comparison with HEVC for intra coding, up to 13% BD-rate savings for the all intra configuration.
2D nonlocal sparse representation for image denoising

Two dimensional (2D) sparse representation provides promising performance in image denoising by cooperatively exploiting horizontal and vertical features inherent in images by two dictionaries. In this paper, we first propose integrating the 2D sparse model with clustering and nonlocal regularization into a unified variational framework, defined as 2D nonlocal sparse representation (2DNSR), for optimization. Within this framework, we then present a dictionary learning method for image denoising which jointly decomposes groups of similar noisy patches on subsets of 2D dictionaries. We finally present a 2DNSR-based algorithm for image denoising. Experimental results on image denoising show our proposed 2D nonlocal sparse representation outperforms the 2D sparse model and achieves competitive performance to state-of-the-art nonlocal sparse models whereas with much less memory costs.
TenSR: Multi-dimensional Tensor Sparse Representation

The conventional sparse model relies on data representation in the form of vectors. It represents the vector-valued or vectorized one dimensional (1D) version of an signal as a highly sparse linear combination of basis atoms from a large dictionary. The 1D modeling, though simple, ignores the inherent structure and breaks the local correlation inside multidimensional (MD) signals. It also dramatically increases the demand of memory as well as computational resources especially when dealing with high dimensional signals. In this paper, we propose a new sparse model TenSR based on tensor for MD data representation along with the corresponding MD sparse coding and MD dictionary learning algorithms. The proposed TenSR model is able to well approximate the structure in each mode inherent in MD signals with a series of adaptive separable structure dictionaries via dictionary learning. The proposed MD sparse coding algorithm by proximal method further reduces the computational cost significantly. Experimental results with real world MD signals, i.e. 3D Multi-spectral images, show the proposed TenSR greatly reduces both the computational and memory costs with competitive performance in comparison with the state-of-the-art sparse representation methods. We believe our proposed TenSR model is a promising way to empower the sparse representation especially for large scale high order signals.
Learning to Detect Video Saliency With HEVC Features.

Saliency detection has been widely studied to predict human fixations, with various applications in computer vision and image processing. For saliency detection, we argue in this paper that the state-of-the-art High Efficiency Video Coding (HEVC) standard can be used to generate the useful features in compressed domain. Therefore, this paper proposes to learn the video saliency model, with regard to HEVC features. First, we establish an eye tracking database for video saliency detection, which can be downloaded from https://github.com/remega/video_database. Through the statistical analysis on our eye tracking database, we find out that human fixations tend to fall into the regions with large-valued HEVC features on splitting depth, bit allocation, and motion vector (MV). In addition, three observations are obtained with the further analysis on our eye tracking database. Accordingly, several features in HEVC domain are proposed on the basis of splitting depth, bit allocation, and MV. Next, a kind of support vector machine is learned to integrate those HEVC features together, for video saliency detection. Since almost all video data are stored in the compressed form, our method is able to avoid both the computational cost on decoding and the storage cost on raw data. More importantly, experimental results show that the proposed method is superior to other state-of-the-art saliency detection methods, either in compressed or uncompressed domain.