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The era of intelligent transportation systems is evolving, which amends the way to deploy smart vision systems. One prominent challenge is to maintain the quality of highway roads for the safety issues. The manual road crack inspection is laborious, time-consuming, and inaccurate. A robust and automatic crack detection is needed to overcome these issues. Many deep learning based automatic crack detections have been developed. However, the majority of models suffer from inaccurate boundary detection due to the irregular shapes of the edge. Thus, the edge detection plays an important role to detect the gradient of the images. This study focuses on the refinement process, in which the pre-computed crack detection is refined to enhance the detection result with more accurate boundaries. The proposed refinement involves the image gradient as an additional feature. The vision transformer is adopted as the core layer to refine the crack in patch-wise manner. The multi-head attention in the transformer enhances the ability of the refinement network to find the relation of each processed patch. Extensive experiments have shown that the proposed method is able to generate superior crack boundaries than that of the state-of-the-art methods.

Discriminative correlation filter-based algorithms have recently demonstrated prominent advantages in the community of computer visual tracking, due to their ability to convert ridge regression problems in the frequency domain for creating solutions efficiently, which has attracted a great deal of attention and spurred new research. High precision and robustness have always been the goals of visual tracking. However, during the tracking process, target objects often encounter sophisticated scenarios such as fast motion and occlusion. During this period, erroneous tracking information will be generated and delivered to the next frame for updating; the information will seriously deteriorate the overall tracking model. To address the problem mentioned above, in this paper, we propose an accurate model self-adaptive update method based on a discriminative correlation filter framework. The proposed tracking method is achieved by utilizing the peak score of a response map generated by the discriminative correlation filter as a dynamic threshold with comparisons to its PSR (peak side-lobe ratio) scores, and then the comparative results are used as the differentiated condition for updating the translation filter and scale filter model. In addition, multiple hand-crafted features such as HOG (histogram of oriented gradient), CN (color names), and HOI (histogram of local intensities) are fused self-adaptively for comprehensive feature representation, which further improve tracking performance. We evaluate the performance of the proposed tracker on OTB benchmark datasets; the experimental results demonstrate that the proposed tracker performs favorably against most state-of-the-art discriminative correlation filter-based trackers including some methods follow deep learning paradigm, and the effectiveness of updating the model self-adaptive is verified.

Normally, modeling 3D face is an effective way for pose-invariant recognition, yet its expensive computation significantly discourages potential applications. In this paper, a simple and fully automatic panoramic image-based pose-invariant face recognition method is proposed to present excellent accuracy with low complexity. In this paper, a face shape model with local morphing treatment is first constructed and considered as the alignment standard to deal with all of the possible geometric distortion problems. During the recognition phase, a proposed systematically designed algorithm with morphing and the selection function are both utilized to significantly ease the negative effects of various poses within ±45° in yaw and ±22.5° in pitch. As demonstrated in experimental results, a similar accuracy as that of the 3D start-of-the-arts is achieved with much less computational complexity.

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.

Ocular recognition is expected to provide a higher flexibility in handling practical applications as oppose to the iris recognition, which only works for the ideal open-eye case. However, the accuracy of the recent efforts is still far from satisfactory at uncontrollable conditions, such as eye blinking which implies any poses of eyes. To address these issues, the skin texture, eyelids, and additional geometrical features are employed. In addition, to achieve higher accuracy, sequential forward floating selection is utilized to select the best feature combinations. Finally, the non-linear support vector machine is applied for identification purpose. Experimental results demonstrate that the proposed algorithm achieves the best accuracy for both open eye and blinking eye scenarios. As a result, it offers greater flexibility for the prospective subjects during recognition as well as higher reliability for security.

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 ( $\alpha $ , $\beta $ ). 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.

This paper presents a new simple and efficient technique for secret sharing. This technique inherits the effectiveness of Progressive Visual Secret Sharing (PVSS), in which the quality of recovered secret image is improved if more shared images are stacked together using the eXclusive-OR (XOR) operation. The proposed method also extends the usability of PVSS approach into the Friendly Visual Secret Sharing (PVSS). The content of shared image is converted into more friendly appearances rather than in the noise-like shared image. The proposed method offers promising result for grayscale and color image. It also produces recovered secret image in the lossless condition. The proposed method can be regarded as a good candidate for secret sharing.

Image segmentation is one of the popular techniques for vision application, i.e., retrieving object information from an image. Segmentation not only provides the class and location of an object, but the associated contour as well. Recent advances of the segmentation deploy the encoder and decoder architectures with skip connection, and also utilize the edge information to obtain the best contour. This work focuses on maintaining the information flow from skip connection, and also identifies suited feature from the bottom layer. The feature selector can be also deployed in edge information for yielding segmentation contour. Designed filters are also employed to analyze the best feature for the reconstruction purpose. Experiment results show that the proposed scheme can improvise the performance of the simple ENet to achieve a superior IoU from 51.3% to 57.9%.

The two key factors in a biometric identification system are its high identification rate and convenience of device usage. In a finger-vein identification task, these two problems often occur since the captured device of finger-vein image should accommodate the high identification rate as well as the easy-to-use device design. The finger-vein is visually invisible inside the human skin. This work develops a new finger-vein capturing device using Near-Infrared (NIR) LED light and proposes an efficient technique for finger-vein identification. The vein image may contain noise and shadows due to device lighting conditions. Parametric-Oriented Histogram Equalization (POHE) is utilized to enhance image contrast and reduce the noise effect. This work also discusses normalized issues related to the angle correction of the finger edge and Region of Interest (ROI) for width normalization. In the experimental result, the proposed method yields a clear finger-vein pattern with a superior identification rate in the recognition task compared to the state-of-the-art methods.

Local image contrast enhancement methods extract more informative details comparing with the global methods. Yet, the traditional schemes require to access all of the pixels in a local region to obtain the cumulative distribution function (cdf) for further enhancement, i.e., adaptive histogram equalization (AHE), and thus the time complexity drastically increases accordingly when the block size increases. Consequently, some speed-up ingenuities such as block-wise strategy and simple proportionally averaging manner are employed to cope with this issue, yet serious blocking artifact or low contrast issues are accompanied. In this study, a local enhancement method, namely corrected parametric-oriented histogram equalization (CPOHE), is proposed to effectively yield enhanced results with high contrast and artifact-free texture using the concept of integral image, and further correct the accompanied distortion. As documented in the experimental results, the proposed method provides a high practical value for applied in visual perception, biometric, and tracking/detection applications.

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.

In recent years, wavelet transform has been widely applied in multimedia signal processing and digital watermarking is involved for ensuring security. This study presents a blind wavelet-based watermarking incorporated with the Human Visual System (HVS), which embeds watermarks into detail-subband coefficients. Since imperceptibility is the most significant issue in watermarking, the approximation band is maintained constant, while the detail subbands are modified to carry information. The perceptual embedded weights for all subbands are determined according to the Just Noticeable Distortion (JND) criterion. The strength of the modification is investigated to provide a balanced result between robustness and image quality. In the decoder, the Least-Mean-Square (LMS) is employed to predict the original detail-subband coefficients and then extract the embedded watermarks. As documented in the experimental results, the proposed method provides good robustness and excellent image quality.

A new image restoration method for improving the quality of halftoning-BTC images.The sparsity-based approach utilizes the double learned dictionaries in the noise reduction.Experimental results demonstrate that the proposed method is superior to former schemes. This paper presents a new image restoration method for improving the quality of halftoning-Block Truncation Coding (BTC) decoded image in a patch-based manner. The halftoning-BTC decoded image suffers from the halftoning impulse noise which can be effectively reduced and suppressed using the Vector Quantization (VQ)-based and sparsity-based approaches. The VQ-based approach employs the visual codebook generated from the clean image, whereas the sparsity-based approach utilizes the double learned dictionaries in the noise reduction. The sparsity-based approach assumes that the halftoning-BTC decode image and clean image share the same sparsity coefficient. In the sparse coding stage, it uses the halftoning-BTC dictionary, while in the reconstruction stage, it exploits the clean image dictionary. As suggested by the experimental results, the proposed method outperforms in the halftoning-BTC image reconstructed when compared to that of the filtering approaches.

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.

This paper presents a new approach to derive the image feature descriptor from the dot-diffused block truncation coding (DDBTC) compressed data stream. The image feature descriptor is simply constructed from two DDBTC representative color quantizers and its corresponding bitmap image. The color histogram feature (CHF) derived from two color quantizers represents the color distribution and image contrast, while the bit pattern feature (BPF) constructed from the bitmap image characterizes the image edges and textural information. The similarity between two images can be easily measured from their CHF and BPF values using a specific distance metric computation. Experimental results demonstrate the superiority of the proposed feature descriptor compared to the former existing schemes in image retrieval task under natural and textural images. The DDBTC method compresses an image efficiently, and at the same time, its corresponding compressed data stream can provide an effective feature descriptor for performing image retrieval and classification. Consequently, the proposed scheme can be considered as an effective candidate for real-time image retrieval applications.

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.

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.

The need of copyright protection and rightful ownership become very urgent in the fast growing Internet environment. The watermarking offers a convenient way to hide specific information via an imaging system for the consumer electronic devices such as digital camera, scanner, and printer. Numerous efforts have been devoted in the Singular Value Decomposition (SVD)-based image watermarking schemes which embed the visual watermark image into the host image before publishing for public usage. However, the main drawback of the SVD-based image watermarking is its false positive problem of which an attacker can easily claim and obtain the correct watermark from an unauthorized image. In this paper, we proposed a new SVD-based image watermarking by embedding the principal component of a watermark into the host image of block based manner using spread spectrum concept. The experimental results demonstrate that the proposed method overcomes the false positive problem, achieves a high payload, and outperforms the former reliable SVD-based watermarking.

The pedestrian detection is a popular research field in recent years, yet the low-resolution issue is rarely discussed for yielding detection accuracy for drivers. In this study, a hierarchical pedestrian detection system is proposed to cope with this issue. In which, two independent features, orientation and magnitude, are adopted as descriptors for pedestrians. Moreover, the proposed probability-based pedestrian mask pre-filtering (PPMPF) is utilized to initially filter out non-pedestrian regions meanwhile retaining most of the real pedestrians. In experimental results, the use of the two proposed features can provide superior performance than the former well-known histogram of oriented gradient (HOG; high accuracy) and the edgelet (high processing efficiency) simultaneously without carrying their lacks. Moreover, the PPMPF can also boost the processing efficiency by a factor of around 2.82 in contrast to the system without this pre-filtering strategy. Thus, the proposed method can be a very competitive candidate for intelligent surveillance applications.

Moving object detection is a fundamental task on intelligent video surveillance systems, because it provides a focus of attention for further investigation. Thus, video object segmentation, which extracts the shape information of moving object from a video sequence, is a key operation for surveillance system. 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.

This paper presents a new way to index a color image by exploiting the low complexity of the Ordered-Dither Block Truncation Coding (ODBTC) for generating the image features. Image content descriptor is directly constructed from two ODBTC quantizers and the corresponding bitmap image without performing the decoding process. The color co-occurrence feature (CCF) derived from the ODBTC quantizers captures the color distribution and image contrast in block based manner, while the Bit Pattern Feature (BPF) characterizes image edges and visual patterns. The similarity between two images can be easily determined based on their CCF and BPF under a specific distance metric measurement. A metaheuristic algorithm, namely Particle Swarm Optimization (PSO), is employed to find the optimum similarity constants and improve the retrieval accuracy. Experimental results demonstrate that the proposed indexing method is superior to the former Block Truncation Coding (BTC) image retrieval system and the other existing methods. The ODBTC method offers an effective way to index an image in a content-based image retrieval system, and simultaneously it is able to compress an image efficiently. Thus, this system can be a very competitive candidate in image retrieval applications.

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.

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.

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.

Nowadays, most multimedia is stored in compressed bit stream format to save the storage space or transmission time. This study proposes a novel technique for embedding flexible amounts of data in the bit map 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 cell averaging and the second round of dithering 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.

In this paper, the impact of the lips for identity recognition is investigated. In fact, it is a challenging issue for identity recognition solely by the lips. In the first stage of the proposed system, a fast box filtering is proposed to generate a noise-free source with high processing efficiency. Afterward, five various mouth corners are detected through the proposed system, in which it is also able to resist shadow, beard, and rotation problems. For the feature extraction, two geometric ratios and ten parabolic-related parameters are adopted for further recognition through the support vector machine. Experimental results demonstrate that, when the number of subjects is fewer or equal to 29, the correct accept rate (CAR) is greater than 98%, and the false accept rate (FAR) is smaller than 0.066%. (CAR95.02%, FAR0.095% # Subjects ≤ 57). Moreover, the processing speed of the overall system achieves 34.43 frames per second, which meets the real-time requirement. Thus, the proposed system can be an effective candidate for facial biometrics applications when other facial organs are covered or when it is applied for an access control 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.

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.

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.

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.

The paper discusses a data-hiding approach that embeds a secret pattern into two or more halftone images using adaptive noise balanced error diffusion.

In this paper, a watermarking scheme, called majority-parity-guided error-diffused block truncation coding (MPG-EDBTC), is proposed to achieve high image quality and embedding capacity. EDBTC exploits the error diffusion to effectively reduce blocking effect and false contour which inherently exhibit in traditional BTC. In addition, the coding efficiency is significantly improved by replacing high and low means evaluation with extreme values substitution. The proposed MPG-EDBTC embeds a watermark simultaneously during compression by evaluating the parity value in a predefined parity-check region (PCR). As documented 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. Nowadays, most multimedia is compressed before it is stored. It is more appropriate to embed information such as watermarks during compression. The proposed method has been proved to solve effectively the inherent problems in traditional BTC, and provide excellent performance in watermark embedding.

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.

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.

This work proposes a novel technique, named embeddable cells selection (ECS), for embedding flexible amounts of data in a ready-made dithered halftone image, while still achieving good quality results. The encoder embeds the secret data in the high-frequency regions with nearly the same number of black and white pixels and low pixel connections. A blind decoding technique without prior knowledge of the original dithered image but with some side information is adopted in the decoder. The inverse halftoning and the second round of halftoning are 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 halftone image can also be reconstructed in the decoder. Furthermore, by recording the embedded positions, this method can guard against distortions caused by tampering, cropping, and print-and-scan attacks.

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.

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.

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

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.

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.

Block Truncation Coding (BTC) image compression technique is capable of extraordinary computational efficiency for high-framerate configurations under the trend of high image resolution. However, the recent development such as the Dot-Diffused BTC (DDBTC) method still encounters various challenges of visual artifacts, including impulsive noise, false contours and blocking effects. In this study, a new method, the Near-Aperiodic DDBTC (NADDBTC) technique, is proposed to analyze the tradeoff and balance between computational efficiency and visual artifacts. It is mainly because of the quantization levels and the co-optimized Class Matrix (CM) and Diffused Matrix (DM) during image compression procedure. In addition, the tiling method for bitmap generation is optimized for aperiodic compressed results. Experimental results show that this method is capable of excellent image quality and visual perception, as well as processing efficiency, at a level similar to the DDBTC technique by taking advantage of parallel processing in dot diffusion. We propose an adaptive strategy to derive quantization levels.The periodic artifact of former DDBTC is eased.Artifacts of former BTCs such as blocking effect and impulsive noise are eased.Proposed method achieves the best visual quality comparing with existing halftoning-based BTCs.

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.

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 reordered image to determine the transmitting order and then progressively reconstructs the dithered image. In addition, the dithered images are further compressed by lossy and lossless procedures. The experimental results demonstrate high-quality reconstructions while maintaining low transmitted bit rates. Moreover, an objective error criterion obtained by LMS optimization is also presented in this study. As documented in the experiments, the error criterion is quite consistent to the basic characteristic of human visual system. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 121–127, 2006.

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.

A low computational complexity noise-balanced error diffusion (NBEDF) technique is proposed for embedding watermarks into error-diffused images. The visual decoding pattern can be perceived when two or more similar NBEDF images are overlaid, even in a high activity region. Also, with the modified improved version of NBEDF, two halftone images can be made from two totally different gray-tone images, and still provide a clear and sharp visual decoding pattern. With self-decoding techniques, we can also decode the pattern with only one NBEDF image. However, the NBEDF method is not so robust to damage due to printing or other distortions. Thus, a kernels-alternated error diffusion (KAEDF) technique is proposed. By using them alternately in the halftone process, we find that two well-known kernels (Jarvis, J.F. et al., 1976; Stucki, P., 1981) are compatible. In the decoder, because the spectral distributions of Jarvis and Stucki kernels are different in the 2D fast Fourier transform domain, we use the cumulative squared Euclidean distance criterion to determine each cell in a watermarked halftone image belonging to either Jarvis or Stucki, and then decode the watermark. Furthermore, because the detailed textures of Jarvis and Stucki patterns are somewhat different in the spatial domain, the lookup table (LUT) technique is also used for fast decoding. From simulation results, the correct decoding rates for both techniques are high and extremely robust, even after printing and scanning processes. Finally, we extend the hybrid NBEDF and KAEDF algorithms to two color EDF halftone images, where 8 independent KAEDF watermarks and 16 NBEDF watermarks can be inserted and still achieve a high-quality result.

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 naive 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 $512\times 512$ , 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.

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.

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.

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.