nils-herrmann/clean_scirepeval huggingface dataset:
High-capacity data hiding in halftone images using minimal-error bit searching and least-mean square filter

In this paper, a high-capacity data hiding is proposed for embedding a large amount of information into halftone images. The embedded watermark can be distributed into several error-diffused images with the proposed minimal-error bit-searching technique (MEBS). The method can also be generalized to self-decoding mode with dot diffusion or color halftone images. From the experiments, the embedded capacity from 33% up to 50% and good quality results are achieved. Furthermore, the proposed MEBS method is also extended for robust watermarking against the degradation from printing-and-scanning and several kinds of distortions. Finally, a least-mean square-based halftoning is developed to produce an edge-enhanced halftone image, and the technique also cooperates with MEBS for all the applications described above, including high-capacity data hiding with secret sharing or self-decoding mode, as well as robust watermarking. The results prove much sharper than the error diffusion or dot diffusion methods.
Clustered-Dot Screen Design for Digital Multitoning

Digital multitoning is an extension of halftoning for rendering more than two tones at each pixel for higher image quality. Although a lot of effort has been put in generating dispersed dots previously, the blue-noise feature can hardly be achieved for those printers utilizing the electrophotography (EP) process to avoid the physically unstable isolated dots. To overcome this issue, Chandu et al. proposed a screening method for yielding green-noise dot clusters, yet noisy multitone texture was accompanied. This degrades the visual quality and the stability of tone rendering. In this paper, a significantly improved homogeneity of clustered dots can be achieved by the proposed screening method based upon the new inter-iterative clustered-dot direct multi-bit search algorithm. Compared with the former approaches, the inter-iteration design leads to less error by the updated initial multitone patterns. As demonstrated in the experimental results, both of the high homogenous multitone texture and less noisy perception at all absorptance levels are offered in contrast to the former Chandu et al.'s results. The high-quality output proves it as a very competitive candidate for EP printers, e.g., laser printers.
Vehicle Verification Using Features From Curvelet Transform and Generalized Gaussian Distribution Modeling

This paper presents a new feature descriptor for vehicle verification. The object detection scheme generates the vehicle hypothesis (candidate) that requires subsequent confirmation in the vehicle verification stage with specific feature descriptors. In the procedure of vehicle verification, an image descriptor is generated from the statistical parameter of the curvelet-transformed (CT) subbands. The marginal distribution of CT output is a heavy-tailed bell-shaped function, which can be approximated as Gaussian, Laplace, and generalized Gaussian distribution (GGD) with high accuracy. The maximum likelihood estimation (MLE) produces the distribution parameters of each CT subband for the generation of the image feature descriptor. The classifier then assigns a class label for the vehicle hypothesis based on this descriptor information. As documented in the experimental results, this feature descriptor is effective and outperforms the existing methods in the vehicle verification tasks.
Improved Pair Toggling Data Hiding by Cooperating Human Visual System in Halftone Images

An improved data hiding in halftone images with cooperating pair toggling human visual system (PTHVS) is presented in this paper. An objective halftone image quality evaluation method based on the human visual system obtained by least-mean-square (LMS) is also introduced. By rigorously searching the optimum toggled pixels with the proposed human visual LMS-trained filter, the proposed method is proven to be superior to the data hiding smart pair toggling (DHSPT), proposed by Fu and Au, in image quality under a number of tested halftone images. The tested halftone images include ordered dithering, Floyd error diffusion, Jarvis error diffusion, and Stucki error diffusion images. Moreover, the proposed method offers high embedded capacity, and it is flexible to deal with different capacity applications.
Improved Hand Tracking System

This paper presents an improved hand tracking system using pixel-based hierarchical-feature AdaBoosting (PBHFA), skin color segmentation, and codebook (CB) background cancellation. The proposed PBH feature significantly reduces the training time by a factor of at least 1440 compared to the traditional Haar-like feature. Moreover, lower computation and high tracking accuracy are also provided simultaneously. Yet, one of the disadvantages of the PBHFA is the false positive which is the consequence of the appearance of complex background in positive samples. To effectively reduce the false positive rate, the skin color segmentation and the foreground detection by applying the CB model are catered 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.
Cascaded Background Subtraction Using Block-Based and Pixel-Based Codebooks

This paper presents a cascaded scheme with block-based and pixel-based codebooks for background subtraction. The codebook is mainly used to compress information to achieve 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 (BTC), and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove the most noise without reducing the True Positive (TP) 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 (FP) rate. Moreover, this study also presents a color model and a match function which can classify an input pixel as shadow, highlight, background, or foreground. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former approaches.
A New Model-Based Digital Halftoning and Data Hiding Designed With LMS Optimization

This work employs the well known least-mean-square (LMS) method to design an adaptive filter to produce high-quality halftone images. The filter can be regarded as a transformation medium between original gray level images and corresponding halftone images. Experimental results indicate that the proposed LMS-designed halftoning offers the extra benefit of edge enhancement. Since a halftone image is typically used in printing, a modified printer model, which can coordinate with the proposed LMS-designed halftoning, is proposed to eliminate the harm caused by the dot-gain effect in printing. Moreover, two data hiding applications, the direct embedding LMS-designed halftone technique (DELDH) and the information sharing LMS-designed halftone technique (ISLDH), are proposed to demonstrate the performance of the proposed LMS-designed halftoning. The experimental results show that, both techniques can be used with the proposed modified printer model to achieve excellent image quality and decoded visual patterns
A Unified Data Embedding and Scrambling Method

Conventionally, data embedding techniques aim at maintaining high-output image quality so that the difference between the original and the embedded images is imperceptible to the naked eye. Recently, as a new trend, some researchers exploited reversible data embedding techniques to deliberately degrade image quality to a desirable level of distortion. In this paper, a unified data embedding-scrambling technique called UES is proposed to achieve two objectives simultaneously, namely, high payload and adaptive scalable quality degradation. First, a pixel intensity value prediction method called checkerboard-based prediction is proposed to accurately predict 75% of the pixels in the image based on the information obtained from 25% of the image. Then, the locations of the predicted pixels are vacated to embed information while degrading the image quality. Given a desirable quality (quantified in SSIM) for the output image, UES guides the embedding-scrambling algorithm to handle the exact number of pixels, i.e., the perceptual quality of the embedded-scrambled image can be controlled. In addition, the prediction errors are stored at a predetermined precision using the structure side information to perfectly reconstruct or approximate the original image. In particular, given a desirable SSIM value, the precision of the stored prediction errors can be adjusted to control the perceptual quality of the reconstructed image. Experimental results confirmed that UES is able to perfectly reconstruct or approximate the original image with SSIM value after completely degrading its perceptual quality while embedding at 7.001 bpp on average.
Progressive Halftone Watermarking Using Multilayer Table Lookup Strategy

In this paper, a halftoning-based multilayer watermarking of low computational complexity is proposed. An additional data-hiding technique is also employed to embed multiple watermarks into the watermark to be embedded to improve the security and embedding capacity. At the encoder, the efficient direct binary search method is employed to generate 256 reference tables to ensure the output is in halftone format. Subsequently, watermarks are embedded by a set of optimized compressed tables with various textural angles for table lookup. At the decoder, the least mean square metric is considered to increase the differences among those generated phenotypes of the embedding angles and reduce the required number of dimensions for each angle. Finally, the naïve Bayes classifier is employed to collect the possibilities of multilayer information for classifying the associated angles to extract the embedded watermarks. These decoded watermarks can be further overlapped for retrieving the additional hidden-layer watermarks. Experimental results show that the proposed method requires only 8.4 ms for embedding a watermark into an image of size 512 × 512, under the 32-bit Windows 7 platform running on 4GB RAM, Intel core i7 Sandy Bridge with 4GB RAM and IDE Visual Studio 2010. Finally, only 2 MB is required to store the proposed compressed reference table.
Contrast Enhancement Using Stratified Parametric-Oriented Histogram Equalization

A contrast enhancement method termed stratified parametric-oriented histogram equalization (SPOHE) is proposed to effectively provide a regional enhanced effect without visual artifacts, e.g., halo or blocking artifacts, which is normally incurred in the former simplified enhancement methods. First, the stratified sampling theory is applied to uniformly sample the original image through many divided strata with the size defined by the two parameters (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>). Second, the required statistical information is efficiently derived through the integral image concept. Finally, the corrected SPOHE is also proposed to further improve the contrast with limited tradeoff computations. The experimental results demonstrate that the proposed scheme yields a cumulative distribution function similar to the actual one for an accurate contrast enhancement performance while significantly reducing the computational complexity. Moreover, compared with the former speed-oriented methods, good contrast and artifact-free results can be achieved simultaneously.
Classified-Filter-based Post-Compensation Interpolation for Color Filter Array demosaicing

In this paper, a classified-based post-compensation algorithm for Color Filter Array (CFA) demosaicing is proposed. This technique can be used for improving the image quality of the interpolated results obtained by other CFA images. First, each pixel is classified according to its neighborhood texture variance and angle. Then, different Least-Mean-Square (LMS) filters are trained to adopt for dealing pixels of various characteristics. As documented in the experimental results, the proposed scheme can substantially boost the image quality; in addition, a better visual perceptual can be obtained. Notably, the proposed method can be considered as effective post-compensation by applying for any former schemes to yield an even better image quality.
Watermarking in Conjugate Ordered Dither Block Truncation Coding Images

In this work, a novel method based on Block Truncation Coding (BTC) and halftoning technique is proposed for embedding watermarks into compressed images. Block truncation coding is a simple and efficient image compression technique. However, it yields images of unacceptable quality and significant blocking effects are seen when the block size used increases. A modified technique known as Ordered Dither Block Truncation Coding (ODBTC) is proposed to solve the above problems while maintaining the same compression capability. In addition, ODBTC can also be used to embed a watermark into the compressed image. This technique incorporates void-and-cluster method with block truncation coding and ordered dithering to carry out watermarking. Experimental results have indicated that the resulting image quality is better and the algorithm less complex as than traditional block truncation coding. Many forms of attacks are also carried out and confirm the robustness of the proposed methods.
Parallel and element-reduced error-diffused block truncation coding

Block Truncation Coding (BTC) is an efficient compression technique for its inherent simple coding strategy. However, the annoying blocking effect and false contour accompanied in high coding gain configurations make the applications relatively limited compares to some up-to-date compression schemes. For this, Error-Diffused Block Truncation Coding (EDBTC) is proposed to solve these problems and obtain satisfactory results. Unfortunately, the EDBTC sacrifices the parallel advantage of traditional BTC. Moreover, the number of diffused directions of EDBTC can be reduced to obtain higher efficiency. For these, the Interlaced Error-Diffused Block Truncation Coding (IEDBTC) is proposed in this work to claim back the parallel advantage. In addition, the diffused elements are also reduced from four to two with the proposed optimization procedure while preserving the image quality.
Data hiding in halftone images with secret-shared dot diffusion

In this study, a low-complexity watermarking for embedding watermark into two or more halftone images with dot diffusion is proposed. The first halftone image is obtained by traditional dot diffusion, and the others are achieved by applying the proposed Secret-Shared Dot Diffusion technique (SSDDF). The visual decoding pattern can be observed when these similar dot-diffused images are printed on transparencies and then overlaid each other. Better decoded result can be obtained by computer-aid XNOR operation. Compared to the Dot Diffusion with Nonlinear Thresholding (DDNT) proposed by Taheri et al, the correct decoding rate can be improved with the proposed approach. Moreover, two extensions called dual SSDDF (DSSDDF) and Adaptive SSDDF (ASSDDF) are proposed to provide better decoding rates and compromised image quality.
Blocking effect and halftoning impulse suppression in an ED/OD BTC image with optimized texture-dependent filter sets

Block Truncation Coding (BTC) has been considered as a highly efficient compression technique for decades. However, the annoying false contour caused by a low bit rate configuration is a key problem. Two recently proposed halftoning-based BTC, namely the Error-Diffused BTC (EDBTC) and the Ordered-Dither BTC (ODBTC), can significantly reduce the above issue. However, some defects such as blocking effect and halftoning impulses are also accompanying with the reconstructed image. For this, this work organizes an optimization procedure to generate a number of texture-dependent filter sets to solve these problems. As demonstrated in the experimental results, the proposed strategy can significantly improve the image quality. This improvement makes the former halftoning-based BTC schemes as potential candidates for surveillance and computer vision applications.
Improved dot diffusion using optimized diffused weighting and class matrix

In this work, a high quality halftone image obtained by dot diffusion is proposed to reduce the deficiency gap with the error diffusion. Four kinds of filters with various sizes obtained by least-mean-square (LMS) are also introduced to simulate the human visual system (HVS). These filters are employed in the optimization procedures for class matrix of size 8x8. According to numerous of simulations, an optimized diffused weighting is determined. Many well-known halftone methods, which include direct binary search (DBS), error diffusion, ordered dithering, and previous dot diffusion are also involved for comparisons. As demonstrated in the experiments, the quality of the proposed dot diffusion is close to some error diffusion and is even superior to the well-known Jarvis and Stucki error diffusion or Mese's dot diffusion. Moreover, the dot diffusion inherently has the parallel processing advantage, which provides much higher executing efficiency than DBS or error diffusion.
Inverse halftoning with variance classified filtering

Inverse halftoning is a key technology to yield a continuous tone image from a halftone image. The main application is to make some image enhancement or further compression of halftones more feasible. Many former approaches have been proposed in the literature. Among these, a recent method proposed by Chung and Wu using edge-based lookup table achieves good image quality, where the edge feature is adopted to refine the trained Look-Up Table (LUT). However, it has three deficiencies, including 1) the edge features are limited in some predefined cases, which cannot full represent every potential possibility, 2) the lookup table grows exponentially when extreme grayscales are attempted to be recorded, and 3) the trained lookup table cannot fully include all the cases, which leaves some halftone patterns in practice have no associated output grayscale. Chang et al.'s method employed one trained filter to compensate the halftone patterns that are not recorded in LUT. However, one filter cannot fully characterize the full textures in an image. In this study, the halftone patterns are classified according to its variance and then used to train the corresponding filter sets, which are then employed to provide higher prediction accuracy by inner product with the corresponding halftone patterns. As documented in the experimental results, the proposed inverse halftoning provides excellent performance in image quality and memory consumption than former approaches.
Adaptive block truncation coding image compression technique using optimized dot diffusion

Block truncation coding (BTC) has been considered as a highly efficient compression technique for decades, but the blocking artifact is its main issue. The halftoning-based BTC has significantly eased this issue, yet an apparent impulse noise artifact is accompanied. In this study, an improved BTC, termed adaptive dot-diffused BTC (ADBTC), is proposed to further improve the visual quality. Also, this method provides an additional flexibility on the compression ratios determination in contrast to the former fixed and few number of configuration possibilities. As documented in the experimental results, the proposed method achieves the superior image quality regarding the five various objective IQA methods. As a result, it is a very competitive approach for the needs of both high frame rate and high-resolution image compression.
Complementary Steganography in Error-Diffused Block Truncation Coding Images

Block truncation coding is an efficient compression technique while offering good image quality. Nonetheless, the blocking effect inherent in BTC causes severe perceptual artifact in high compression ratio applications. In this paper, an error-diffused block truncation coding (EDBTC) is proposed to solve this problem. According to the EDBTC, the error caused by the difference between the original grayscale pixel value and the correspondingly high or low mean substitute is diffused to the predefined neighborhood, and hence the average grayscale will be maintained invariably. In addition, since the compressed data are widely distributed in the Internet transmission, the extra message conveying in a secret way also highly raises attention recently. In this paper, we propose the complementary steganography in error- diffused block truncation coding (CSEDBTC), which cooperates with error diffusion to achieve the objective of secret communication in BTC images. As documented in the experimental results, a low complexity with good image quality is obtained.
Majority-Parity-Guided Watermarking for Block-Truncated Images

In this paper, a watermarking scheme, called Majority-Parity-Guided Error-Diffused Block Truncation Coding (MPG-EDBTC), is proposed to achieve with high image quality and embedded capacity. The main problem of traditional BTC is its poor quality over configurations of high compression ratio. To overcome such problem, the extreme pixel values are employed to substitute both high and low means. The quantized error is also compensated by adjusting the neighboring pixels. With these strategies, the image quality and processing efficiency are improved. Moreover, the watermark is embedded by evaluating the parity value in a pre-defined Parity-Check Region (PCR). As seen in the experimental results, the proposed scheme can provide good robustness, image quality, and processing efficiency. Finally, the proposed MPG-EDBTC is extended to embed multiple watermarks and achieves excellent image quality, robustness, and capacity as well. Nowadays, most multimedia is stored in compressed format. It is more appropriate to embed information such as watermarks in compressed domain. The proposed method has been proved to solve effectively the inherent problems in traditional BTC, and provide excellent performance in watermark embedding.
High efficient contrast enhancement using parametric approximation

In this study, a local contrast enhancement method, namely Parametric-Oriented Histogram Equalization (POHE), is proposed to effectively yield enhanced results. In general, the grayscale distribution of a specific region in an image can be modeled with a kernel function such as the Gaussian, and thus the corresponding estimated cumulative distribution function (cdf) can be considered as the transformation function for contrast enhancement. The required parameters, however, still need to access all of the pixels in the corresponding region, and thus consume a huge amount of computations. To cope with this, the concept of integral image is adopted to effectively derive the required parameters. In the experimental results, former well-known speed-oriented methods are adopted for comparison, and the results demonstrate that the proposed methods can provide high practical value for biometric and tracking/detection these active issues who desire high efficiency.
Watermarking in Halftone Images with Parity-Matched Error Diffusion

A halftone watermarking technique of high capacity, robustness, and capacity flexibility is presented in this paper. This parity-matched error diffusion (PMEDF) method is capable of achieving an embedded capacity as high as 6.25% to 25% with good image quality and without the original image as the reference to decode the watermark. As the experimental results demonstrated, this technique is able to guard against degradation due to cropping, tampering, and printed-and-scanned process in error-diffused halftone images.