nils-herrmann/clean_scirepeval huggingface dataset:
Vehicle verification using Generalized Gaussian Distribution feature descriptor

A new feature descriptor based on the statistical modeling of wavelet output is proposed for vehicle verification problem. The Generalized Gaussian Distribution (GGD) offers accurate statistical fitting for the real and imaginary part of Gabor and Dual-Tree Complex Wavelet Transform (DT-CWT) output. Consequently, the descriptor is simply composed from the GGD parameters of Gabor and DT-CWT output by means of Maximum Likelihood Estimation (MLE) approach under GGD assumption. Experimental result shows that the proposed feature descriptor outperforms the former existing method in the vehicle verification using SVM classifier.
High Efficiency Digital Halftoning with Two-Element Error Kernel

A high efficiency and good quality error-diffused halftoning is proposed in this paper. An objective halftone image quality evaluation method based on human visual system obtained by least-mean-square is also introduced. The error diffusion algorithm, first introduced by Floyd and Steinberg in 1975, is currently the most popular method of digital halftoning. However, some annoying shortcomings, such as patches of regular textures, worm effect, and computational complexity still need to be improved. Ostromoukhov proposed a three-element error diffusion solving the problems described above, and achieved excellent performance. In this paper, we further reduced the complexity with the proposed two-element error diffusion by cooperating with the proposed LMS-trained quality evaluation. As demonstrated in experiments, the proposed technique further reduced the complexity by 33% compared to Ostromoukhov's method, and it is still comparable in image quality.
Improved Block Truncation Coding Based on the Void-and-Cluster Dithering Approach

Block truncation coding (BTC) is an efficient technology for image compression. An improved BTC algorithm, namely ordered dither block truncation coding (ODBTC), is presented in this study. In order to provide better image quality, the void-and-cluster halftoning is combined with the BTC. The ODBTC results show that the image quality is improved when it is operated in high coding gain applications. Another feature of the ODBTC is the dither array look up table (LUT), which significantly reduces the complexity compared to the BTC.
Halftone-Image Security Improving Using Overall Minimal-Error Searching

For image-based data hiding, it is difficult to achieve good image quality when high embedding capacity and 100% data extraction are also demanded. In this study, the proposed method, namely, overall minimal-error searching (OMES) is developed to meet the aforementioned requirements. Moreover, the concept of secret sharing is also adopted to distribute watermarks into multiple halftone images, and the embedded information can only be extracted when all of the marked images are gathered. The OMES modifies the halftone values at the same position of all host images with the trained substitution table (S-Table). The S-Table makes the original combination of these halftone values as another meaningful combination for embedding watermark, which is the key part in determining the image quality. Thus, an optimization procedure is proposed to achieve the optimized S-Table. Two different encoders, called error-diffused-based and least-mean-square-based approaches are also developed to cooperate with the proposed OMES to cope with high processing speed and high image quality applications, respectively. Finally, for resisting the issues caused by the print-and-scan attack, such as zooming, rotation, and dot gain effect, a compensation correction procedure is also proposed. As demonstrated in the experimental results, the proposed approach provides good image quality, and is able to guard against some frequent happened attacks in printing applications.
Content-based image retrieval with ordered dither block truncation coding features

This paper presents a technique for Content-Based Image Retrieval (CBIR) by exploiting the low complexity advantage of the Ordered-Dither Block Truncation Coding (ODBTC) for generating image content descriptors. The two image features, namely Color Co-occurrence Feature (CCF) and Bit Pattern Features (BPF), are generated from ODBTC encoded data streams (without really performing an image compression or decoding process) to measure the similarity between two images. Experimental results show that the proposed method is superior to the Block Truncation Coding (BTC) image retrieval system and other former methods, and prove that the ODBTC scheme is not only suited for image compression for its simplicity, but also offers a conveniently way for image indexing in the content-based image retrieval system.
High speed multi-layer background subtraction

Moving object detection is an important and fundamental step for intelligent video surveillance systems, since it provides a focus of attention for post-processing. In this study, a multi-layer codebook-based background subtraction model is proposed to cope with high resolution video sequences for the detection of moving objects. Combining the multi-layer block-based strategy and the feature extraction of blocks, the proposed method can remove most of the background, including non-stationary (dynamic) background, and significantly increase the processing efficiency. Moreover, pixel-based classification is adopted for refining the results of block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. The experimental results demonstrate that the proposed multi-layer codebook-based background subtraction method (for standard definition video sequences) can provide a high precision and efficient processing speed for moving objects detection. Moreover, extensive experiments have been conducted to compare with various former schemes, and the superiority of the proposed method shows that it can be a very effective candidate for real-time intelligent video surveillance applications.
Reversible data hiding in highly efficient compression scheme

Nowadays, most multimedia is stored in compressed bit stream format to save the storage apace or transmission time. This study proposes a novel technique for embedding flexible amounts of data in the bitmap of the improved Ordered Dither Block Truncation Coding (ODBTC) image, where the ordered dithering is used to dither the quantized BTC image to avoid the annoying false contour and blocking effect inherently existed in BTC image. Moreover, the LUT strategy is also used to significantly reduce the complexity. The inverse halftoning and the second round of halftoning are employed as the key steps in locating the embedded information bits. Experimental results demonstrate that an objective good quality image with flexible capacity and reasonable complexity is obtained. Moreover, the correct decoding rate of 100% is maintained, and the original host ODBTC image can also be reconstructed in the decoder when needed, which significantly boosts the flexibility in image quality control.
Secret Communication Using JPEG Double Compression

Protecting privacy for exchanging information through the media has been a topic researched by many people. Up to now, cryptography has always had its ultimate role in protecting the secrecy between the sender and the intended receiver. However, nowadays steganography techniques are used increasingly besides cryptography to add more protective layer to the hidden data. In this letter, we show that the quality factor in a JPEG image can be an embedding space, and we discuss the ability of embedding a message to a JPEG image by managing JPEG quantization tables (QTs). In combination with some permutation algorithms, this scheme can be used as a tool for secret communication. The proposed method can achieve satisfactory decoded results with this straightforward JPEG double compression strategy.
Improved Low-Complexity Algorithm for 2-D Integer Lifting-Based Discrete Wavelet Transform Using Symmetric Mask-Based Scheme

Wavelet coding performs better than discrete cosine transform in visual processing. Moreover, it is scalable, which is important for modern video standards. The transpose memory requirement and operation speed are the two major concerns in 2-D lifting-based discrete wavelet transform (LDWT) implementation. This letter presents a novel algorithm, called 2-D symmetric mask-based discrete wavelet transform (SMDWT), to improve the critical issue of the 2-D LDWT, and then obtains the benefit of low-latency reduced complexity, and low transpose memory. The SMDWT also has the advantages of reduced complexity, regular signal coding, short critical path, reduced latency time, and independent subband coding processing. Furthermore, the 2-D LDWT performance can also be easily improved by exploiting an appropriate parallel method inherent to SMDWT. The proposed method has a significantly better lifting-based latency and complexity in 2-D DWT than normal 2-D 5/3 integer LDWT without degradation in image quality. The algorithm can be applied to real-time image/video applications.
Object matching using hybrid modified RGB color model and HRR-based background detection

This study presents a pixel-wise statistical approach to distinguish every pixel into foreground, background, shadow or highlight for background subtraction and foreground detection. The modified RGB color model is proposed to effectively reduce misclassifying the foreground pixels into highlights. The modified Highest Redundancy Ratio (HRR)-based background update method is also proposed to overcome the lighting variation and slow motion object problems in background reconstruction. In tracking procedure, a decision function with low computational complexity is proposed to sequentially evaluate the objectspsila correlation between consecutive frames. The decision function consists of the objectspsila centroid distances, objectspsila area differences, and objectspsila overlapping areas between current frame and previous frame. As documented in experimental results, the proposed method can achieves high matching rate, which is great advantageous in surveillance systems.
Texture orientation modulation for halftoning watermarking

In this paper, a halftoning-based watermarking scheme with high data capacity and image quality is presented. Three types of watermarks of various pixel-depths, including 1-bit, 2-bit, and 3-bit, are able to be embedded without prominently damaging the image quality. To achieve high marked image quality, the parallel-oriented high efficient Direct Binary Search (DBS) halftoning is adopted to cooperate with the proposed Orientation Modulation (OM) method. In the decoder, the Least-Mean-Square-trained (LMS-trained) filters are adopted to extract the features of marked images in the frequency domain, and the naïve Bayes classifier is employed to analyze the extracted features and further decode the watermark information. Experimental results demonstrate that the proposed DBS-based OM encoding scheme provides excellent image quality, high processing efficiency, and high robustness to adapt to practical printing application.
Novel Data Hiding and Blind Decoding in Dithering Halftone Images

In this paper, we propose a novel technique for embedding flexible amounts of data into a dithering halftone image and still achieving a high quality result. In the encoder, we reserve the high frequency regions with nearly the same number of black and white pixels by calculating the pixel connection. The secret data is embedded into these regions. In the decoder, a blind decoding technique is applied without prior knowledge of the original dithering image but with some side information. From the experimental results, a subjective high quality image with flexible capacity is obtained. Moreover, the correct decoding rate is kept 100%, and the original dithered halftone image can also be reconstructed in the receiver
Face gender recognition with halftoning-based adaboost classifiers

This paper presents a new face gender recognition scheme by enjoying the benefit from the dot diffusion among weak classifiers in recognition phase for a low resolution and non-aligned thumbnail image. The main problem of the former Adaboost approaches is that each weak classifier simply offers a binary decision, which fails to compensate the decision error by diffusing it to the rest weak classifiers. To cope with this, this work exploits the dot-diffused-based Adaboost to solve this problem. As documented in the experimental results, with the examination of Feret and CMU databases, this paper has shown that the proposed scheme is an effective candidate in improving the recognition accuracy rate and the efficiency of the overall system process for face gender recognition.
Dot-Diffused Halftoning With Improved Homogeneity

Compared with the error diffusion, dot diffusion provides an additional pixel-level parallelism for digital halftoning. However, even though its periodic and blocking artifacts had been eased by the previous works, it was still far from satisfactory in terms of the blue noise spectrum perspective. In this paper, we strengthen the relation among the pixel locations of the same processing order by an iterative halftoning method, and the results demonstrate a significant improvement. Moreover, a new approach of deriving the averaged power spectrum density is proposed to avoid the regular sampling of the well-known Bartlett's procedure which inaccurately presents the halftone periodicity of certain halftoning techniques with parallelism. As a result, the proposed dot diffusion is substantially superior to the state-of-the-art parallel halftoning methods in terms of visual quality and artifact-free property, and competitive runtime to the theoretical fastest ordered dithering is offered simultaneously.
Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval.

This paper presents an effective image retrieval method by combining high-level features from convolutional neural network (CNN) model and low-level features from dot-diffused block truncation coding (DDBTC). The low-level features, e.g., texture and color, are constructed by vector quantization -indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate and average recall rate (ARR), are employed to examine various data sets. As documented in the experimental results, the proposed schemes can achieve superior performance compared with the state-of-the-art methods with either low-or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.
Hybrid hand tracking system

This study presents a hybrid hand tracking system using Pixel-Based Hierarchical-Feature AdaBoosting (PBHFA), skin color segmentation, and codebook background cancellation. The object of this approach is to construct a system which is able to cope with hand detection and further tracking tasks. To reduce the effect of false positive, the skin color segmentation and the foreground subtraction by applying the codebook model are employed for rejecting all of the candidates which are not hand targets. As documented in the experimental results, the proposed system can achieve promising results, and thus it can be considered as an effective candidate in handling practical applications which require hand postures.
Complexity Reduced Face Detection Using Probability-Based Face Mask Prefiltering and Pixel-Based Hierarchical-Feature Adaboosting

The Adaboosting 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. This letter presents a two-stage hybrid face detection scheme using Probability-based Face Mask Pre-Filtering (PFMPF) and the Pixel-Based Hierarchical-Feature Adaboosting (PBHFA) method to effectively solve the above-mentioned problems in cascade Ad aboosting. The two stages both provide far less training time than that of the cascade Adaboosting and thus reduce the computation complexity in face-detection tasks. In particular, the proposed PFMPF can effectively filter out more than 85% nonface in an image and the remaining few face candidates are then secondly filtered with a single PBHF Adaboost strong classifier. 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 two-stage hybrid face detection scheme are employed for practical face-detection tasks, the complexity is still lower than that of the integral-image based approach in the traditional Adaboosting method. Experimental results obtained using the gray feret database show that the proposed two-stage hybrid face detection scheme is significantly more effective than Haar-like features.
A new progressive coding algorithm of dithered images

A novel progressive coding scheme is presented for the efficient display of dithered images. Dithered images are the results of thresholding original gray-level images with dithering screens. After the preprocessing of bit-interleaving, this algorithm utilizes the characteristic of the 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.
Improved Block Truncation Coding using Optimized Dot Diffusion

Block Truncation Coding (BTC) has been considered as a highly efficient compression technique for decades. However, the annoying blocking effect and false contour under low bit rate configuration are its key problems. In this work, an improved BTC, namely Dot-Diffused BTC (DDBTC), is proposed to solve these problems. On one hand, the DDBTC can provide excellent processing efficiency by exploiting the innate parallelism advantage of dot diffusion. On the other hand, the DDBTC can provide excellent image quality by co-optimizing the class matrix and diffused matrix of the dot diffusion. The experimental results demonstrate that the proposed DDBTC is fully superior to the pervious Error-Diffused BTC (EDBTC) in terms of image quality and processing efficiency, and has much better image quality than that of the Ordered-Dither BTC (ODBTC).
Improved Dot Diffusion by Diffused Matrix and Class Matrix Co-Optimization

Dot diffusion is an efficient approach which utilizes concepts of block-wise and parallel-oriented processing to generate halftones. However, the block-wise nature of processing reduces image quality much more significantly as compared to error diffusion. In this work, four types of filters with various sizes are employed in co-optimization procedures with class matrices of size 8 times 8 and 16 times 16 to improve the image quality. The optimal diffused weighting and area are determined through simulations. Many well-known halftoning methods, some of which includes direct binary search (DBS), error diffusion, ordered dithering, and prior dot diffusion methods, are also included for comparisons. Experimental results show that the proposed dot diffusion achieved quality close to some forms of error diffusion, and additionally, superior to the well-known Jarvis and Stucki error diffusion and Mese's dot diffusion. Moreover, the inherent parallel processing advantage of dot diffusion is preserved, allowing us to reap higher executing efficiency than both DBS and error diffusion.
Mean-Classified and Hierarchical Block Matching Watermarking in Fractal Coding

This study presents two watermarking approaches in fractal coding images. The first method, namely mean-classified fractal coding (MCFC), partitions the domain blocks of an image into groups according the mean values. As documented in experimental results, a high embedding capacity can be achieved with good image quality. Moreover, the MCFC is proved of highly robust to against many types of attacks, including JPEG, tampering, and rotating. The second method, namely hierarchical block matching fractal coding (HBMFC), utilizes different domain block sizes to enlarge the searching pool with slightly sacrificing the coding gain. The HBMFC is mainly adapted to fragile watermarking, which can be applied to authentication application to prevent from illegal alteration.
Hierarchical Method for Foreground Detection Using Codebook Model

This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.
Novel robust watermarking technique in dithering halftone images

In this letter, we present a novel robust method for embedding watermarks into dithered halftone images. The method is named paired sub-image matching ordered dithering (PSMOD), of which the decoder is provided with a priori information of the original watermark. The method utilizes the bit and sub-subimage interleaving preprocesses. The experiments show that the technique is sufficiently robust to guard against the cropping, tampering, and printed-and-scanned degradation processes, in either B/W or color dithered images. This technique is also sufficiently flexible for various levels of embedded capacities.
Improved Block Truncation Coding Using Extreme Mean Value Scaling and Block-Based High Speed Direct Binary Search

Block truncation coding (BTC) has been considered as an efficient compression technique for decades. However, the annoying blocking effect and false contour caused by low bit rate configuration are key problems. To solve these problems, many former halftoning-based BTCs are proposed. However, these schemes also induce another impulse noise issue while the previous issues still have room for improvement. To cope with this, the iteration-based halftoning is combined with BTC, namely Direct-Binary-Search BTC (DBSBTC) to solve the aforementioned problems. Moreover, the high-speed DBS halftoning method along with the block-based strategy yield even faster processing speed than some of the former halftoning-based BTC schemes. As documented in the experimental results, the proposed DBSBTC is superior to the former halftoning-base BTC schemes in terms of image quality, and which makes the former schemes as potential candidates for surveillance and computer vision applications.