nils-herrmann/clean_scirepeval huggingface dataset:
Halftone Image Classification Using LMS Algorithm and Naive Bayes

Former research on inverse halftoning most focus on developing a general-purpose method for all types of halftone patterns, such as error diffusion, ordered dithering, etc., while fail to consider the natural discrepancies among various halftoning methods. To achieve optimal image quality for each halftoning method, the classification of halftone images is highly demanded. This study employed the least mean-square filter for improving the robustness of the extracted features, and employed the naive Bayes classifier to verify all the extracted features for classification. Nine of the most well-known halftoning methods were involved for testing. The experimental results demonstrated that the classification performance can achieve a 100% accuracy rate, and the number of distinguishable halftoning methods is more than that of a former method established by Chang and Yu.
An Efficient Fusion-Based Defogging

Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. To overcome this problem, conventional approaches focus mainly on the enhancement of the overall image contrast. However, because of the unspecified light-source distribution or unsuitable mathematical constraints of the cost functions, it is often difficult to achieve quality results. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improve the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The systematic assessments show that this approach is capable of achieving superior defogging and dehazing performance, compared with superior defogging and dehazing performance, compared with the state-of-the-art methods, both quantitatively and qualitatively.
Face gender recognition using improved appearance-based Average Face Difference and support vector machine

This paper presents an improved Appearance-based Average Face Difference (AAFD) scheme for face gender recognition with a low resolution and non-align thumbnail image. The main problem of the former appearance-based approaches is that not all the information is equally important in a face sub-window. Some regions in a face sub-window may have similar feature for both male and female, and some regions may contains hair, background, or noises. Thus, this work exploits the proposed face gender mask to determine the key areas in a face to classify its gender. As documented in the experimental results, with the examination of color Feret database, this method has shown that it is an effective candidate in improving the training time, recognition accuracy rate, and efficiency of overall system process for face gender recognition applications.
Improved block truncation coding using modified error diffusion

Block truncation coding (BTC) is an efficient compression technique, offering good image quality. Nonetheless, the blocking effect inherent in BTC causes severe perceptual artefact when compression ratio is increased. Conversely, error diffusion (EDF) enjoys the benefit of diffusing the quantised error into neighbouring pixels. Consequently, the average tones in any local areas of the error-diffused image are preserved unchanged. Presented is a hybrid approach which combines the proposed modified EDF with BTC. As documented in experimental results, image quality is much better than BTC, and the complexity is even lower than traditional BTC.
High efficient Direct Binary Search using Multiple Lookup Tables

Look-Up Table (LUT) halftoning is an efficient way to construct halftone images, and approximately simulate the dot distribution of the learned halftone image set. In this study, a general mechanism named Multiple Look-Up Table (MLUT) halftoning is proposed to generate the halftones of Direct Binary Search (DBS), while the high efficient characteristic of the LUT is still preserved. In the MLUT, the standard deviation is adopted as an important feature to classify various tables. Moreover, the proposed Quick Standard Deviation Evaluation (QSDE) is employed to yield an extremely low computational complexity in calculating the standard deviation. In the parameter optimization, the autocorrelation is adopted since it can fully characterize the periodicity of dot distribution. Experimental results demonstrate that the visual quality of the proposed method can approximate to that of the DBS which is considered as the best halftoning in terms of image quality, which enable the proposed scheme as a very competitive candidate in coping printing industry.
Multimedia Classification Using Bipolar Relation Graphs

Recent studies on category relations have shown the promising progress in addressing classification problems. Existing works independently consider the known relation and classifier optimization, and thus restrain the room for performance improvement. In this work, a new loss function is proposed to leverage the underlining relations among categories and classifiers. In addition, the bipolar relation (BR) graph is employed to formulate a general form for diverse relations. This bipolar graph is automatically learnt for reliving the constraints which may happen during the cost minimization. Extensive experiments on three benchmarks with various hypotheses and graphs demonstrate that our method can offer a significant performance improvement by jointly learning from both BR graph and hypothesis, in particular on a small training dataset scenario that suffers from severe overfitting problem.
Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.
Two-dimensional nonequilibrium fluid models for streamers

The self-consistent, two-dimensional fluid simulation for describing nitrogen gas prebreakdown phenomena under atmospheric pressure is presented in cylindrically symmetric geometry. The models of the electron dynamics are characterized either by an equilibrium single-moment equation or by a nonequilibrium three-moment equation. A more accurate flux-corrected transport (MAFCT) technique, which provides a solution with steep and varying gradients in large dynamic ranges, is used to solve the electron fluid equations. Included in the step-by-step presentation are the electron density, the space-charge electric field, the electron average velocity, the electron mean energy, and the electron power deposition from the initial stage to the later stage when the ionizing channel bridges the gap. The differences between equilibrium and nonequilibrium fluid models are discussed in terms of the formation of ionizing channels and the propagation of streamers.
Complexity Reduction and Fast Algorithm for 2-D Integer Discrete Wavelet Transform Using Symmetric Mask-Based Scheme

Wavelet coding has been shown to be better than discrete cosine transform (DCT) in image/video processing. Moreover, it has the feature of scalability, which is involved in modern video standards. This work presents novel algorithms, namely 2-D symmetric mask-based discrete wavelet transform (SMDWT), to improve the critical issue of the 2-D lifting-based discrete wavelet transform (LDWT), and then obtains the benefit of low latency, high-speed operation, and low temporal memory. The SMDWT also has the advantages of high-performance embedded periodic extension boundary treatment, reduced complexity, regular signal coding, short critical path, reduced latency time, and independent subband coding processing. Moreover, the 2-D lifting-based DWT performance can also be easily improved by exploiting appropriate parallel method inherently in SMDWT. Comparing with the normal 2-D 5/3 integer lifting-based DWT the proposed method significantly improves lifting-based latency and complexity in 2-D DWT without degradation in image quality. The algorithm can be applied to real-time image/video applications, such as JPEG2000, MPEG-4 still texture object decoding, and wavelet-based Scalable Video Coding (SVC).
Contact-free hand geometry identification system

This paper presents an approach for personal identification using hand geometrical features, in which the infrared illumination device is employed to improve the usability of this hand recognition system. In the proposed system, prospective users can place their hands freely in front of the camera without any pegs or templates. The system can also work in normal environment, since no dark background is required. The idea behind the proposed system is to locate the tip of the middle finger and then rotate the palm image according to the tip point. Moreover, additional 12 important points are further identified and 30 features are defined from the information of those points. In addition, the Support Vector Machine (SVM) was used for distinguishing various hands. Experimental result shows an average Correct Identification Rate (CIR) of 98.75%, which is an encouraging consequence for a contact-free hand geometry identification system.
Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

This paper presents a new approach to index color images using the features extracted from the error diffusion block truncation coding (EDBTC). The EDBTC produces two color quantizers and a bitmap image, which are further processed using vector quantization (VQ) to generate the image feature descriptor. Herein two features are introduced, namely, color histogram feature (CHF) and bit pattern histogram feature (BHF), to measure the similarity between a query image and the target image in database. The CHF and BHF are computed from the VQ-indexed color quantizer and VQ-indexed bitmap image, respectively. The distance computed from CHF and BHF can be utilized to measure the similarity between two images. As documented in the experimental result, the proposed indexing method outperforms the former block truncation coding based image indexing and the other existing image retrieval schemes with natural and textural data sets. Thus, the proposed EDBTC is not only examined with good capability for image compression but also offers an effective way to index images for the content-based image retrieval system.
Improved directional lifting-based discrete wavelet transform for low resolution moving object detection

In this study, the current state-of-the-art in moving objects segmentation for intelligent video surveillance has been surveyed. An efficient Modified Directional Lifting-based 9/7 Discrete Wavelet Transform (MDLDWT) structure is proposed to further reduce the computational cost and preserve the fine shape information in low resolution image. Although perfect moving object detection in a practical environment is a challenging task due to the vague object shape issues in the low resolution configuration, the experimental results document that the proposed low complexity MDLDWT scheme can provide more precise detection rate for multiple moving objects, and the fine shape information can be effectively preserved for the real-time video surveillance applications in both indoor and outdoor environments.
Near-aperiodic dot-diffused block truncation coding

In this study, an improved Block Truncation Coding (BTC) image compression scheme, namely Near-Aperiodic Dot-Diffused BTC (NADDBTC), is described. Firstly, the existing regular structures for the generation of bitmap are completely modified for aperiodic compressed results. Moreover, an adaptive quantization levels selection strategy and two parameters Class Matrix (CM) and Diffused Matrix (DM) for image compression are developed and co-optimized. The improvements produce results of superior image quality. Furthermore, the adaptive quantization levels are introduced for balanced false contour, impulsive noise, and blocking artifact. Experimental results demonstrate that the proposed NADDBTC is capable of providing excellent image quality and visual perception, as well as processing efficiency, similar to DDBTC by exploiting the innate parallelism advantage of dot diffusion.
Content-based image retrieval using direct binary search block truncation coding features

This paper presents a new image feature descriptor derived from the Direct Binary Search Block Truncation Coding (DBSBTC) data-stream without requiring the decoding process. Three image feature descriptors, namely Color Autocorrellogram Feature (CAF), Legendre Chromaticity Moment Feature (LCMF), and Local Halftoning Pattern Feature (LHPF), are simply constructed from the DBSBTC min quantizer, max quantizer, and its corresponding bitmap image, respectively. The similarity between two images can be measured using these descriptors under specific distance metric. The proposed method yields better image retrieval performance compared to the former Block Truncation Coding (BTC) and existing schemes under the natural and textural image database in the grayscale and color space. The DBSBTC performs well for image compression, at the same time, it gives an effective discriminative feature in the image retrieval task.
New Class Tiling Design for Dot-Diffused Halftoning

In this paper, a new class tiling designed dot diffusion along with the optimized class matrix and diffused matrix are proposed. The result of this method presents a nearly periodic-free halftone when compared to the former schemes. Formerly, the class matrix of the dot diffusion is duplicated and orthogonally tiled to fulfill the entire image for further thresholding and quantized-error diffusion, which accompanies subsequent periodic artifacts. In our observation, this artifact can be solved by manipulating the class tiling with comprising rotation, transpose, and alternatively shifting of the class matrices. As documented in the experimental results, the proposed dot diffusion has been compared with the former halftoning methods with parallelism in terms of image quality, processing efficiency, periodicity, and memory consumption; the proposed dot diffusion exhibits as a very competitive candidate in the printing/display market.
Paired Subimage Matching Watermarking Method on Ordered Dither Images and Its High-Quality Progressive Coding

In this paper, we present two novel robust methods for embedding watermarks into dithered halftone images. The first method is named paired subimage matching ordered dithering (PSMOD), of which the decoder is provided with a priori information of the original watermark, and the corresponding application is copyright protection. The other method, blind paired subimage matching ordered dithering (BPSMOD), does not require the knowledge of the original watermark, and the main application is secret communication. Both methods utilize the bit and sub-subimage interleaving preprocesses. The experiments show that both techniques are sufficiently robust to guard against the cropping, tampering, and print-and-scan degradation processes, in either B/W or color dithered images. Both techniques are also sufficiently flexible for various levels of embedded capacities. Furthermore, a novel progressive coding scheme is also presented in this paper for the efficient display of dithered images. After the preprocessing of bit-interleaving, this algorithm utilizes the characteristic of reordered image to determine the transmitting order and then progressively reconstructs the dithered image. Moreover, the dithered images are further compressed by lossy and lossless procedures. The experimental results demonstrate high-quality reconstructions while maintaining low transmitted bit rates.
Reversible Data Hiding Based on Histogram Modification of SMVQ Indices

This work presents a novel reversible data-hiding scheme that embeds secret data into a transformed image and achieves lossless reconstruction of vector quantization (VQ) indices. The VQ compressed image is modified by the side-matched VQ scheme to yield a transformed image. Distribution of the transformed image is employed to achieve high embedding capacity and a low bit rate. Moreover, three configurations, under-hiding, normal-hiding, and over-hiding schemes, are utilized to improve the proposed scheme further for various applications. Experimental results demonstrate that the proposed scheme significantly enhances the compression ratio and embedding capacity. Experimental results also show that the proposed scheme achieves the best performance among approaches in literature in terms of the compression ratio and embedding capacity.
Efficient Halftoning Based on Multiple Look-Up Tables

Look-up table (LUT) halftoning is an efficient way to construct halftone images and approximately simulate the dot distribution of the learned halftone image set. In this paper, a general mechanism named multiple look-up table (MLUT) halftoning is proposed to generate the halftones of direct binary search (DBS), whereas the high efficient characteristic of the LUT is still preserved. In the MLUT, the standard deviation is adopted as an important feature to classify various tables. In addition, the proposed quick standard deviation evaluation is employed to yield an extremely low computational complexity in calculating the standard deviation. In the parameter optimization, the autocorrelation is adopted because it can fully characterize the periodicity of dot distribution. Experimental results demonstrate that the dot distribution generated by the proposed method approximates to that of the DBS, which enables the proposed scheme as a very competitive candidate in the copying and printing industry.
License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques

License plate localization (LPL) and character segmentation (CS) play key roles in the license plate (LP) recognition system. In this paper, we dedicate ourselves to these two issues. In LPL, histogram equalization is employed to solve the low-contrast and dynamic-range problems; the texture properties, e.g., aspect ratio, and color similarity are used to locate the LP; and the Hough transform is adopted to correct the rotation problem. In CS, the hybrid binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms.
A New Digital Haiftoning and Data Hiding Designed with LMS Optimization

In this paper, the well known least-mean-square (LMS) method is applied for designing an adaptive filter used to produce a high quality halftone image. The experimental results show that the proposed LMS-designed halftoning achieves the extra benefit of edge enhancement and eliminates the annoying worm effect in error-diffused image. In addition, we provide two data hiding applications, named direct embedding LMS-designed halftone technique (DELDH) and information sharing LMS-designed halftone technique (ISLDH) to demonstrate the performance of the proposed LMS-designed halftoning. As the experimental results demonstrated, both techniques achieve excellent image quality and decoded visual patterns
Streamer radius model and its assessment using two-dimensional models

The variable radius method is proposed to approximate the radius of the ionization channel in the one and a half-dimensional (1.5-D) fluid models for studying streamer development, in which the unreasonable constant radius in the traditional 1.5-D fluid models is corrected. The streamer development and propagation between the 1.5-D fluid model with the variable radius method and the two-dimensional (2-D) fluid model using the same initial and external conditions are compared. The radius in each stage of streamer development from the 1.5-D fluid model with the variable radius method shows agreement to a certain degree with that of the 2-D fluid model. The purpose of this paper is not to negate the role of the 2-D fluid models, but to explore the potential of the 1.5-D fluid models and make them more useful and accurate as well as to understand the evolution of streamer radius. The streamer development from the 1.5-D fluid model with the variable radius method not only maintains simplicity of the 1.5-D fluid models, but also presents agreement with the 2-D fluid models for streamers.
On the use of image quality estimators for improved JPEG2000 coding

An IQA algorithm that performs well in predicting image quality on current image-quality databases does not guarantee the same success in a coding context. During JPEG2000 encoding, it is possible to generate images with vastly different visual qualities depending on how the distortions are allocated to different frequency bands. To test the efficacy of IQA algorithms for JPEG2000 encoding, we collected subjective rankings of JPEG2000-compressed images in which a fixed amount of distortion was allocated to the subbands in various ways. Human subjects provided quality rankings of the distorted images to determine the best and worst strategies of distortion allocation on a per-image basis. The results of this experiment revealed that, in general, higher quality images were achieved by allocating most of the distortion to the finest scale and the least to the coarse-to-mid scales. We also tested the ability of various IQA algorithms in predicting these subjective rankings. Even though these algorithms have been shown to perform reasonably well on JPEG2000-compressed images in popular image-quality databases (e.g., LIVE, CSIQ), our analysis revealed that these algorithms failed to predict the correct rankings on many of the images. Based on the findings from this analysis and the framework of Most Apparent Distortion (MAD), we present a new IQA algorithm MADDWT, designed specifically for JPEG2000 coding which uses local DWT coefficient statistics to predict the perceived distortion due to subband quantization.