nils-herrmann/clean_scirepeval huggingface dataset:
High Efficiency and Contrast Enhanced Haiftoning with Hybrid Ordered Dithering and Error Diffusion Model

A high efficiency and contrast enhanced halftoning is presented in this paper. The contrast of digital image highly depends on the nature of the ambient lighting and the quality of the lens. The required processing time skyrockets when a high resolution and quality printed work is needed. In this paper, we employ the benefits of high speed ordered dithering and high quality error diffusion to simultaneously solve the problems we described above. As the experimental results demonstrate, this technique is able to enlarge the dynamic range of the histogram and improve execution efficiency to around 15%~47% with the tested images
Robust watermarking with kernels-alternated error diffusion and weighted lookup table in halftone images

A halftone watermarking method of high quality, robustness, and capacity flexibility is presented in this paper. An objective halftone image quality evaluation method based on the human visual system obtained by least-mean-square is also introduced. In the encoder, the kernels-alternated error diffusion (KAEDF) is applied. This is able to maintain the computational complexity at the same level as ordinary error diffusion. Compared with Hel-Or (2001) using ordered dithering, the proposed KAEDF yields a better image quality through using error diffusion. We also propose a weighted lookup table (WLUT) in the decoder instead of LUT, as proposed by Pei and Guo (2003), so as to achieve a higher decoded rate. As the experimental results demonstrated, this technique is able to guard against degradation due to tampering, cropping, rotation, as well as print-and-scan processes in error-diffused halftone images.
High Capacity Data Hiding for Error-Diffused Block Truncation Coding

Block truncation coding (BTC) is an efficient compression technique with extremely low computational complexity. However, the blocking and false contour effects are two major deficiencies in BTC which cause severe perceptual artifacts. The former scheme, error-diffused BTC (EDBTC), can significantly improve the above issues through the visual low-pass compensation on the bitmap, which thus widens its possible application market, yet the corresponding security issue may limit its value. In this paper, a method namely complementary hiding EDBTC is developed to cope the above issue. This paper is managed by firstly discussing when a single watermark is embedded, and then multiple watermarks are employed to test the limitation of the proposed scheme. Herein, an adaptive external bias factor is employed to control the watermark embedding, and this factor also affects the image quality and robustness simultaneously. Experimental results demonstrate that the proposed method only requires an extremely small external bias factor to carry watermarks, which enables a high capacity scenario without significantly damaging image quality.
A Complete Printer Model in Error-Diffused Halftone Images

Generally speaking, the researches in model-based halftoning can be divided into error-diffusion based and iteration based methods. In this study, we propose a modified printer model to eliminate the damage from dot gain effect in printed halftone images. The proposed printer model cooperates with error diffusion and totally solves the dot gain problem in just one processing pass for all kinds of printed dot radii. Moreover, the modified printed image inherently needs fewer printed dots than the image obtained by traditional halftone method. With this modified printer model, it also offers an additional benefit of saving the cartridge expenditure. As documented in the experimental results, the average cartridge saving is around 38.58% with nature images.
Pixel-Based Hierarchical-Feature face detection

In this paper, the Pixel-Based Hierarchical-Feature Adaboosting (PBHFA) method is presented. The purpose of this approach is the reduction of computation complexity in face-detection tasks. The Adaboosting method has attracted attention for its efficient face-detection performance. However, in the training process, the large number of possible Haar-like features in a standard sub-window becomes time consuming, which makes specific environment feature adaptation extremely difficult. For this object, the PBHFA is proposed as a possible solution. Given a M × N sub-window, the number of possible PBH features is simplified down to a level less than M × N, which significantly reduces the length of the training period by a factor of 1500. Moreover, when the trained PBH features are employed for practical face-detection tasks, the hierarchically structural pattern matching also has lower complexity than that of the integral-image based approach in the traditional Adaboosting method. As documented in experimental results, with the MIT-CMU profile test set are examined, the proposed PBH features have shown significantly more effective than Haar-like features.
Reversible data hiding scheme with high embedding capacity using semi-indicator-free strategy

This work presents a novel reversible data-hiding scheme which embeds secret data into a side matched Vector Quantization (SMVQ)-compressed image, and achieves lossless reconstruction of a Vector Quantization (VQ)-compressed image. The rather random distributed histogram of a VQ-compressed image can be re-located to locations close to zero by SMVQ prediction. Thus, fewer bits can be used to encode SMVQ indices with very small values, and no indicator is required to encode these indices, which yields extra hiding space to hide secret data. Consequently, high embedding capacity and low bit rate scenarios can be deposited. Experimental results demonstrate the effectiveness and reversibility of the proposed scheme. Moreover, in terms of the embedding rate, the bit rate, and the embedding capacity, experimental results show that the performance of the proposed scheme is better than those of the former data hiding schemes for VQ-based and VQ/SMVQ-based compressed images.
Variance-Classified Capacity Watermarking Using Discrete Cosine Transform

This study presents a robust and semi-blind watermarking by embedding information into low frequency AC coefficients of discrete cosine transform (DCT). Since the imperceptibility is the most significant issue in watermarking, the DC value is maintained unchanged. The proposed methods utilize the DC values of the neighboring blocks to predict the AC coefficients of the center block. The low frequency AC coefficients are modified to carry watermark information. The least mean squares (LMS) is employed to yield the intermediate filters, cooperating with the neighboring DC coefficients to precisely predict the original AC coefficients. Two watermarking methods, namely watermark embedding in the predicted AC coefficient (WEPAC) and watermark embedding in the original AC coefficient (WEOAC), are presented in this study. Moreover, many attacks are addressed to show the robustness of the proposed methods.
Information Hiding Based on Block Match Coding for Vector Quantization-Compressed Images

In this paper, a novel highly efficient lossless data hiding scheme is proposed to achieve the goal of hiding secret data into vector quantization (VQ)-compressed images that can be losslessly reconstructed after the secret data is extracted in the decoder. For VQ-compressed images, the index-modifying and the side-match VQ (SMVQ) techniques can be applied to encode indices and hide secret data. In general, data hiding accompanied by the SMVQ technique can yield a higher embedding capacity and a lower bit rate, yet more time consuming. In contrast, data hiding accompanied by the index-modifying technique can yield a lower embedding capacity and a higher bit rate, yet less time consuming. To possess the advantages of the two techniques while removing the shortcomings, the neighboring processed indices are employed to speed up the processes of generating state codebooks required for encoding and hiding. To evaluate the effectiveness of this approach, various test images are employed in the experiments. As documented in the experimental results, it is shown that the performance of the proposed scheme is superior to former schemes in terms of compression ratio, embedding rate, processing efficiency, and embedding capacity.
Watermarking in Halftone Images with Kernels-Alternated Error Diffusion and Haar Wavelet Transform

A halftone watermarking method of high quality, robustness, and capacity flexibility is presented in this paper. An objective halftone image quality evaluation method based on the human visual system obtained by least-mean-square is also introduced. In the encoder, the kernels-alternated error diffusion (KAEDF) is applied. This scheme is able to maintain the computational complexity at the same level as an ordinary error diffusion. Compared with Hel-Or using ordered dithering, the proposed KAEDF yields a better image quality through using error diffusion. Moreover, the Haar wavelet transform (HWT) decoding is employed instead of lookup table (LUT), so as to reduce the computational complexity. As documented in the experimental results, this technique is able to guard against degradation due to tampering, cropping, rotation, as well as print-and-scan processes in error-diffused halftone images.
Memory-Efficient Hardware Architecture of 2-D Dual-Mode Lifting-Based Discrete Wavelet Transform

Memory requirements (for storing intermediate signals) and critical path are essential issues for 2-D (or multidimensional) transforms. This paper presents new algorithms and hardware architectures to address the above issues in 2-D dual-mode (supporting 5/3 lossless and 9/7 lossy coding) lifting-based discrete wavelet transform (LDWT). The proposed 2-D dual-mode LDWT architecture has the merits of low transpose memory (TM), low latency, and regular signal flow, making it suitable for very large-scale integration implementation. The TM requirement of the <formula formulatype="inline"><tex Notation="TeX">$N\times N$</tex></formula> 2-D 5/3 mode LDWT and 2-D 9/7 mode LDWT are <formula formulatype="inline"><tex Notation="TeX">$2N$</tex></formula> and <formula formulatype="inline"><tex Notation="TeX">$4N$</tex></formula>, respectively. Comparison results indicate that the proposed hardware architecture has a lower lifting-based low TM size requirement than the previous architectures. As a result, it can be applied to real-time visual operations such as JPEG2000, motion-JPEG2000, MPEG-4 still texture object decoding, and wavelet-based scalable video coding applications.
Limitation investigation toward lips recognition

In this paper, the impact of the lips for facial recognition is investigated. In the first stage of the proposed system, a Fast Box Filtering (FBF) is proposed to generate a noise-free source with high processing efficiency. Afterward, five various mouth corners are detected though the proposed system, in which it is also able to resist beard and rotation problems. For the feature extraction, two geometric ratios and 10 parabolic related parameters are adopted for further recognition through the Support Vector Machine (SVM). Experimental results demonstrate that when the number of subjects is fewer or equal to 36, the Correct Accept Rate (CAR) is greater than 98%, and the False Accept Rate (FAR) is smaller than 0.064% (CAR>;95.6%, FAR<;0.083%| #Subjects ≤ 54). Moreover, the processing speed of the overall system achieves 34.43 fps (frame/sec) which meets the real-time requirement.
Prediction-Based Watermarking Schemes for DCT-Based Image Coding

This study presents two predicted-based watermarking schemes, namely Ahead AC-Predicted Watermarking (AAPW) and Post AC-Predicted Watermarking (PAPW), by embedding information into low frequency AC coefficients of Discrete Cosine Transform (DCT). The proposed methods utilize the DC values of the neighboring blocks to predict the AC coefficients of the center block. The low frequency AC coefficients are modified to carry watermark information. The Least Mean Squares (LMS) is employed to yield the intermediate filters for cooperating with the neighboring DC coefficients to predict the original AC coefficients. During the LMS filter training, the training blocks are classified into different categories according to their texture angles and variances. The classified trained filter sets are then used to predict the AC coefficients even more precisely. As documented in the experimental results, the image quality and the embedded capacity of the proposed schemes are superior to former methods in the literature. Moreover, many attacks are addressed to show the robustness of the proposed methods.
3-Step Bi-Level Images for Multiscaling and Lossless Compression

In this paper, we propose a new technique for converting gray level images into bi-level images, while the most significant information in original gray level image are preserved. From experimental, the reconstructed quality is better than Otsu and Kapur's methods. The well-known digital halftoning is traditionally applied to get a high quality binary image for specified output devices, e.g., printers. However, the halftone image is not suitable for some image processing, such as downsampling or further compression. These problems can be solved with the proposed 3-step bi-leveling, which including Sobel high active regions detection, LMS-designed halftoning, local and global smooth region binarization
Tone-Replacement Error Diffusion for Multitoning

Error diffusion is an efficient halftone method for mainly being applied on printers. The promising high image quality and processing efficiency endorse it as a popular and competitive candidate in halftoning and multitoning applications. The multitoning is an extension of halftoning, adopting more than two-tone levels for the improvement of the similarity between an original image and the converted image. Yet, the banding effect, indicating the areas with discontinuous tone level, disturbs the visual perception, and thus seriously degrades image quality. To solve the banding effect, the tone-replacement strategy is proposed in this paper. As documented in the experimental results, excellent tone-similarity as that of the original image and promising reconstructed dot-distribution can be provided simultaneously. Comparing with the former banding-free methods, the apparent improvements/features suggest that the proposed method can be a very competitive candidate for multitoning applications.
Oriented Modulation for Watermarking in Direct Binary Search Halftone Images

In this paper, a halftoning-based watermarking method is presented. This method enables high pixel-depth watermark embedding, while maintaining high image quality. This technique is capable of embedding watermarks with pixel depths up to 3 bits without causing prominent degradation to the image quality. To achieve high image quality, the parallel oriented high-efficient direct binary search (DBS) halftoning is selected to be integrated with the proposed orientation modulation (OM) method. The OM method utilizes different halftone texture orientations to carry different watermark data. In the decoder, the least-mean-square-trained filters are applied for feature extraction from watermarked images in the frequency domain, and the naïve Bayes classifier is used to analyze the extracted features and ultimately to decode the watermark data. Experimental results show that the DBS-based OM encoding method maintains a high degree of image quality and realizes the processing efficiency and robustness to be adapted in printing applications.
Banding effect removal for digital multitoning

Error diffusion is an efficient halftone method for mainly being applied on printers. The promising high image quality and processing efficiency endorse it as a popular and competitive candidate in halftoning and multitoning applications. The multitoning is an extension of halftoning, adopting more than three tone levels for improving the similarity between an original image and the converted image. Yet, the banding effect, indicating the areas with only one tone level, disturbs the visual perception, and thus seriously degrades image quality. To cope with the banding effect, the tone replacement strategy is proposed in this study. As documented in the experimental results, excellent tone-similarity as that of the original image and promising reconstructed dot-distribution can be provided simultaneously. Comparing with the former banding-free methods, the apparent improvements/features suggest that the proposed method can be a very competitive candidate for multitoning applications.
Data hiding in halftone images with noise-balanced error diffusion

In this letter, we propose a low-complexity algorithm for embedding watermarks into two or more error-diffused images. The first one is only a regular error-diffused image, and the others are achieved by applying the proposed noise-balanced error diffusion technique (NBEDF) to the original gray-level image. The visual decoding pattern can be perceived when these two or more similar error-diffused images are overlaid each other. Furthermore, with the proposed modified version of NBEDF, the two halftone images can be made from two totally different gray-tone images and still provide a clear and sharp visual decoding pattern.
A blind wavelet-based watermarking with detail-subband coefficients prediction

Recently, the wavelet transform is widely used in multimedia signal processing applications. To provide security solution, the digital watermarking is involved. This study presents a blind wavelet-based watermarking which cooperates with the human visual system (HVS) embedding watermarks into detail-subband coefficients. Since the imperceptibility is the most significant issue in watermarking, the approximate band is maintained unchanged, while the other detail subbands are modified to carry information. The perceptual embedded weights for all subbands are determined based on the just noticeable distortion (JND) criterion. The strength of the modification is investigated to provide a compromised result between robustness and image quality. In the decoder, the least-mean-square (LMS) is involved to predict the original detail-subband coefficients and then extract the embedded watermarks. As documented in experimental results, the proposed method provides good robustness and excellent image quality.
Incorporating color feature on LBP-based image retrieval

The Local Binary Pattern (LBP) operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification. The LBP-based operator extracts the textural information of an image by considering the neighboring pixel values. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it is less suitable for measuring the similarity of color images with rich color information. This work overcomes this problem by adding an additional color feature, namely Color Histogram Feature (CHF), along with the LBP-based feature in the image retrieval domain. Experimental result shows that the hybrid CHF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases.
Human face age estimation with adaptive hybrid features

This paper presents an appearance-based human face age estimation scheme. The age estimation has become an important study in recently several years. The main issue of the aging process is that it varies across various people, and which makes the age estimation rather challenge. This study combines the shape feature, texture feature, and frequency feature using Active Shape Model (ASM), Radon transform, and Discrete Cosine Transform (DCT) to establish robust adaptive hybrid features for further classification. In estimation stage, the SVM is employed for the proposed hierarchical classification framework and the SVR is also involved for regression. As documented in the experimental results, the proposed method can provide superior performance than former state-of-the-art methods in terms of the MAE with the FG-NET database.
Security attacks on the wavelet transform and singular value decomposition image watermarking

Two vulnerable attacks on the wavelet transform (WT) and Singular Value Decomposition (SVD) based image watermarking scheme are presented in this paper. The WT-SVD-based watermarking is robust against various common image manipulations and geometrical attacks; however, it cannot resist against two security attacks, i.e. an attacker attack which successfully claims the real owner's watermarked image, and owner attack which correctly extracts watermark from any arbitrary image. As proved in this study, the SVD watermarking scheme cannot provide trustworthy evidence in rightful ownership protection. In addition, the robustness of the SVD watermarking scheme in is a result of improper algorithm design.
Nighttime Vehicle Lamp Detection and Tracking With Adaptive Mask Training

In recent years, traffic flow controlling and monitoring have become important issues in intelligent transportation systems (ITSs). For example, vehicle flow management can positively benefit the ITSs from accurate road vehicle detection and counting surveillance systems. In this paper, a high-accuracy vehicle detection mechanism with robust and adaptive vehicle mask training is proposed to effectively detect vehicles in nighttime scenarios. First, a preprocessing step is applied to segment vehicle lamps and filter out reflections. Next, the similarity and symmetric degree of light components are classified into vehicle lamp candidates. A simple fog lamp elimination method is proposed for adapting the proposed method to various road conditions. Then, the mask is trained by a combination of color distribution and weighting information, and it is utilized in the first stage of vehicle detection. The experimental results verify that the proposed method is effective and adaptive for the detection of vehicles at night.
A novel fast algorithm based on SMDWT for visual processing applications

This work presents a fast algorithm, namely 2-D Symmetric Mask-based Discrete Wavelet Transform (SMDWT), to address some critical issues of the 2-D Discrete Wavelet Transform (DWT). Unlike the traditional DWT involving dependent decompositions, the SMDWT itself is subband processing independent, which can significantly reduce complexity. Moreover, DWT cannot directly obtain target subbands, which leads to an extra wasting in transpose memory, critical path, and operation time. These problems can be fully improved with the proposed SMDWT. Nowadays, many applications employ DWT as the core transformation approach, the problems indicated above have motivated researchers to develop fast algorithms for DWT. The proposed SMDWT has been proved as a highly efficient independent processing to yield target subbands which can be applied to real-time visual applications, such as moving object detection and tracking, texture segmentation, image/video compression, and any DWT-based applications.
Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

Moving object detection is an important and fundamental step for intelligent video surveillance systems because it provides a focus of attention for post-processing. A multilayer codebook-based background subtraction (MCBS) model is proposed for video sequences to detect moving objects. Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the nonstationary (dynamic) background and significantly increase the processing efficiency. Moreover, the pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. As a result, the proposed scheme can provide a high precision and efficient processing speed to meet the requirements of real-time moving object detection.