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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.

We propose an effective method to boost the accuracy of multi-person pose estimation in images. Initially, the three-layer fractal network was constructed to regress multi-person joints location heatmap that can help to enhance an image region with receptive field and capture more joints local-contextual feature information, thereby producing keypoints heatmap intermediate prediction to optimize human body joints regression results. Subsequently, the hierarchical bi-directional inference algorithm was proposed to calculate the degree of relatedness (call it Kinship) for adjacent joints, and it combines the Kinship between adjacent joints with the spatial constraints, which we refer to as joints kinship pattern matching mechanism, to determine the best matched joints pair. We iterate the above-mentioned joints matching process layer by layer until all joints are assigned to a corresponding individual. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art schemes and achieves about 1% and 0.6% increase in mAP on MPII multi-person subset and MSCOCO 2016 keypoints challenge.

With the advent of mobile smart devices and ubiquitous network connections, digital images can now be conveniently captured, edited, and shared online worldwide. The ever-increasing number of pictures poses a technically challenging, yet unavoidable problem of efficiently and accurately finding a desired image. In this work, we develop a new image retrieval system based on an image compression technique, namely dot-diffused block truncation coding (DDBTC) with bit probability. Specifically, the color feature derived from color distribution and the bitmap feature (BF) derived from both edges and textures jointly describe the image. The degree of similarity between two images is then measured by their respective color and BFs by using the modified Canberra distance metric. Experimental results show that the proposed feature descriptor achieves superior image retrieval performance as compared to the former DDBTC image retrieval feature and conventional non-DDBTC based features. Additional experiments on image classification verify that the proposed feature descriptor outperforms conventional image classification methods.

Halftone classification is a primary requisite for the perfect reconstruction of binary patterns during inverse halftone process. Majority of the halftone classification techniques are either limited to error diffused halftone or to limited categories, and cannot be generalized to all halftone versions. In this scenario, a new classification approach is proposed based on the premise that the stochastic analysis can uniquely characterize the halftone patterns. The proposed scheme exploits inherent association of halftone patterns and stochastic geometry, and utilizes its spatial and spectral parameters for feature vector construction. Extreme learning machine based multi-classifier model is adopted resulting in rapid and accurate classification. A digital halftone database comprises of 96 reference images along with the 21 varieties of halftone and multitone class is developed. From extensive analysis, it has been validated that the proposed scheme can achieve a 100% classification rate on many halftone versions and exhibit superior performance over the exiting classification techniques.

Digital halftoning is a strategy to convert the color or gray scale image into a printable format. Among several halftoning techniques, error diffusion is one of the conventional and widely adopted method in many printing devices. In further, halftone classification is very important to obtain the perfect reconstruction of binary printed images. The paper attempts to solve this issue by exploiting the intrinsic similarity between the stochastic geometry and halftoning. Feature vectors are constructed using the point process statistic parameters such as directional distribution function and radially averaged power spectral density. Extreme learning machine model is developed and eight varieties of error diffusion halftone images are considered for classification. From the results, it has been validated that the proposed scheme yields better accuracy of 97.5% and it is faster than the existing approaches.

The conventional dual-mode halftoning methods achieve high quality halftone patterns by easing smooth artifact and preserving image details. However, the boundaries between low- and high-frequency image regions still present undesired texture in some particular cases, which significantly degrades the visual quality. In this work, a multi-mode halftoning method is proposed to deal with the object map artifact and boundary artifact simultaneously. In addition, the new absorptance-frequency stacking constraint is also employed to solve the noisy textures of the halftone outputs. As documented in the experimental results, high quality halftone outputs can be obtained, proving that the proposed method can be a competitive candidate for electrophotographic printers.

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.

Compared to 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 previous works, it was still far from satisfactory in terms of the blue noise spectrum perspective. In this work, we strengthen the relationship 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 (APSD) 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., Comment: Accepted to IEEE Trans. on Image Processing

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 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.

This paper presents a low complexity configurable semi-fragile watermarking scheme for content-based H.264/AVC authentication, which allows content-preserving manipulations such as video transcoding techniques, while it is very sensitive to content-changing and frame manipulations. A low cost spatial analysis is exploited to maximize robustness and lessen the incurred distortion. Invariant features are extracted from intra macroblocks, and frame index timing information constituting the content-based authentication codes are embedded/extracted into Quantized DCT (QDCT) coefficients in a Group of Pictures GOP-based fashion. The embedded watermark can be detected and verified by partially decoding intra and inter macroblocks without the prior knowledge of the original video or complete video decoding. Spatial and temporal tampering can be characterized and localized by comparing the extracted and re-generated feature-based watermarks. Experimental results demonstrate that the performance of the proposed scheme is indeed superb in terms of bit-rate and perceptual quality. Moreover, various types of attacks have been simulated and characterized.

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.

This study presents an approach to effectively suppress the annoying false contour inherently introduced in a JPEG compressed image. The false contour is mainly caused by the discrepancy between the original DCT coefficient prior quantization in the encoder and the inverse quantization coefficient in the decoder. In this study, the error diffusion with the optimized error kernels is involved to compensate the quantization errors to the pixels in the neighboring blocks. As documented in the experimental results, the proposed method has been proven to significantly reduce the false contour in a JPEG image.

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.

Formerly, dimensionality reduction techniques are effective ways for extracting statistical significance of features from their original dimensions. However, the dimensionality reduction also induces an additional complexity burden which may encumber the real efficiency. In this paper, a technique is proposed for the reduction of the dimension of samples rather than the features in the former schemes, and it is able to additionally reduce the computational complexity of the applied systems during the reduction process. This method effectively reduces the redundancies of a sample, in particular for those objects which possess partially symmetric property, such as human face, pedestrian, and license plate. As demonstrated in the experiments, based upon the premises of faster speeds in training and detection by a factor of 4.06 and 1.24, respectively, similar accuracies to the ones without considering the proposed method are achieved. The performance verifies that the proposed technique can offer competitive practical values in pattern recognition related fields.

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

This study analyzes the recent image watermarking schemes based on redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD), and shows that in fact they are insecure and cannot be used for protecting the rightful ownership. The RDWT-SVD watermarking directly embeds a grayscale watermark image of the same size with the host image into the singular value matrix of the RDWT-transformed host image, then produces the left and right orthogonal matrices as side information which is later used in the watermark extraction stage. The RDWT-SVD approach enjoys the advantage of the RDWT redundancy to achieve a high embedding capacity, and preserves the watermark imperceptibility by exploiting the SVD stability properties. It is claimed that RDWT-SVD watermarking is robust against several common image processing and geometrical attacks, yet a fundamental flaw in the RDWT-SVD scheme is found, which leads to severe the false positive issue. Three vulnerable attacks should be considered in the RDWT-SVD scheme: (1) An attacker can easily claim the owner watermarked image; (2) the owner has the ambiguity because of the wrong side information usage, and (3) the owner can extract the correct watermark from arbitrary image. Thus, it is important to highlight these attacks when implementing the RDWT-SVD watermarking scheme.

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.

This letter presents a new method to derive the image feature descriptor for vehicle verification. The effectiveness of the proposed feature descriptor is based on the nature of the Gabor filter magnitude that tends to obey the Gamma distribution. The statistical parameters of the Gabor magnitude are computed using the Maximum Likelihood Estimation (MLE), which is later utilized to construct the feature descriptor. Conventionally, the Gabor magnitude is simply modeled by using Gaussian distribution, and thus the image descriptor consists of mean, standard deviation, and skewness values of the Gabor filter magnitude. However, recent investigations found that the skewness parameter is not contributing towards class separation. Based on our observation, the Gamma distribution provides a better statistical fitting to represent the Gabor filter magnitude when compared to the Gaussian distribution. As documented in the experimental results, the proposed feature descriptor yields higher accuracy for vehicle verification when compared to the conventional schemes.

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.

In this study, a high accuracy fingerprint classification method is proposed to enhance the performance in terms of efficiency for fingerprint recognition system. The recognition system has been considered as a reliable mechanism for criminal identification and forensic for its invariance property, yet the huge database is the key issue to make the system obtuse. In former works, the pre-classifying manner is an effective way to speed up the process, yet the accuracy of the classification dominates the further recognition rate and processing speed. In this paper, a rule-based fingerprint classification method is proposed, wherein the two features, including the types of singular points and the number of each type of point are adopted to distinguish different fingerprints. Moreover, when fingerprints are indistinguishable, the proposed Center-to-Delta Flow (CDF) and Balance Arm Flow (BAF) are catered for further classification. As documented in the experimental results, a good accuracy rate can be achieved, which endorses the effectiveness of the fingerprint classification scheme for the further fingerprint recognition system.

An efficient and geometric-distortion-free approach, namely the fast binary robust local feature (FBRLF), is proposed. The FBRLF searches the stable features from an image with the proposed multiscale adaptive and generic corner detection based on the accelerated segment test (MAGAST) to yield an optimum threshold value based on adaptive and generic corner detection based on the accelerated segment test (AGAST). To overcome the problem of image noise, the Gaussian template is applied, which is efficiently boosted by the adoption of an integral image. The feature matching is conducted by incorporating the voting mechanism and lookup table method to achieve a high accuracy with low computational complexity. The experimental results clearly demonstrate the superiority of the proposed method compared with the former schemes regarding local stable feature performance and processing efficiency.

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.

H.264/AVC-based products have grown tremendously in social networks; issues of content-based authentication become increasingly important. This paper presents a blind fragile watermarking scheme for content-based H.264/AVC authentication, which enjoys high sensitivity to typical video attacks. A spatiotemporal analysis is exploited to guarantee a minimum impact on perceptual quality and bit-rate increment. The watermark features are extracted from intra/inter prediction modes of intra/inter macroblocks, constituting the content-based Message Authentication Code (MAC) which is embedded/extracted in a Group-of-Pictures GOP-based fashion utilizing the syntactic elements of the Network Application Layer (NAL) units from the compressed bitstream. It's unnecessary to fully decode a compressed bitstream before the embedding or detection processes. A content-based key is generated to control fragile watermark generation, embedding, extraction, and verification algorithms. Additionally, fragility is ensured by selecting the last nonzero quantized ac residuals for watermark embedding. The embedded watermark can be detected and verified by means of partially decoding intra/inter prediction modes from syntactic elements of the bitstream without the prior knowledge of the original video or complete decoding. Experiment results demonstrate that the performance of the proposed scheme is excellent in terms of bit-rate and perceptual quality. Furthermore, various types of content-preserving and/or content-changing attacks can be detected efficiently.

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

Data hiding is one of the protective techniques for authentication or secret communication through a public and open channel such as the Internet. In this study, a novel lossless data hiding by embedding secret data into a Vector Quantization (VQ)-compressed image is proposed to achieve secret communication and data compression simultaneously. The correlation of neighboring blocks of a VQ-compressed image is explored. It is shown that the neighboring blocks of a VQ-compressed image normally have high mutual correlation. Thus, this scheme employs the neighboring processed compression indices to generate specific sub-codebooks required for encoding and hiding data simultaneously. Since the sizes of sub-codebooks are smaller than that of the original VQ codebook, the encoded size of each index can be significantly reduced. As a result, a great deal of extra free space can be created. Moreover, the original VQ-compressed images can be perfectly recovered after data extraction. To evaluate the effectiveness of this approach, various test images are employed in the experiments. As documented in the experimental results, it is shown that the performance of the proposed scheme is superior to the former schemes in terms of compression ratio, embedding rate, execution time, and embedding capacity.

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.

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.

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.

For visual processing applications, the two-dimensional (2-D) Discrete Wavelet Transform (DWT) can be used to decompose an image into four-subband images. However, when a single band is required for a specific application, the four-band decomposition demands a huge complexity and transpose time. This work presents a fast algorithm, namely 2-D Symmetric Mask-based Discrete Wavelet Transform (SMDWT), to address some critical issues of the 2-D DWT. Unlike the traditional DWT involving dependent decompositions, the SMDWT itself is subband processing independent, which can significantly reduce complexity. Moreover, DWT cannot directly obtain target subbands as mentioned, which leads to an extra wasting in transpose memory, critical path, and operation time. These problems can be fully improved with the proposed SMDWT. Nowadays, many applications employ DWT as the core transformation approach, the problems indicated above have motivated researchers to develop lower complexity schemes for DWT. The proposed SMDWT has been proved as a highly efficient and independent processing to yield target subbands, which can be applied to real-time visual applications, such as moving object detection and tracking, texture segmentation, image/video compression, and any possible DWT-based applications.

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.

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.

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

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.

This study presents two predicted-based watermarking schemes, namely ahead AC-predicted watermarking (AAPW) and post AC-predicted watermarking (PAPW), by embedding information into low frequency AC coefficients of discrete cosine transform (DCT). The proposed methods utilize the DC values of the neighboring blocks to predict the AC coefficients of the center block. The low frequency AC coefficients are modified to carry watermark information. The least mean squares (LMS) is employed to yield the intermediate filters for cooperating with the neighboring DC coefficients to predict the original AC coefficients. During the LMS filter training, the training blocks are classified into different categories according to their texture angles and variances. The classified trained filter sets are then used to predict the AC coefficients even more precisely. As documented in the experimental results, the image quality and the embedded capacity of the proposed schemes are superior to former methods in the literature. Moreover, many attacks are addressed to show the robustness of the proposed methods.

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 study, the ordered dither block truncation coding (ODBTC) is proposed to solve this problem, where the dither array look up table (LUT) approach is proposed to significantly reduce the complexity of the BTC. Moreover, a novel progressive coding scheme is presented for efficiently displaying ODBTC images. The ODBTC utilizes the characteristic of the bit-interleaved bit map to determine the transmitting order and then progressively reconstructs the ODBTC image. In addition, the progressive ODBTC can provide higher compression ratio by toggling and entropy coding treatments. The experimental results demonstrate high-quality reconstructions while maintaining low transmitted bit rates.

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.

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.