AC Coefficients Research Articles

Image compression for quality 3D reconstruction

A 3D mesh can be reconstructed from multiple viewpoint images or from a single structured light image. Lossy compression of such images by standard techniques such as JPEG at high compression ratios lead to 3D reconstruction being adversely affected by artifacts and missing vertices. In this paper we demonstrate an improved algorithm capable of high compression ratios without adversely affecting 3D reconstruction and with minimum data loss. The compression algorithm starts by applying block DCT over the input image, and the transformed data being quantized using an optimized quantization matrix. The quantized coefficients of each block are arranged as a 1D array and saved with other block’s data in a larger matrix of coefficients. The DC coefficients are subject to a first order difference whose values are referred to as residual array. The AC coefficients are reduced by eliminating zeros and saving the non-zero values in a reduced coefficients array using a mask of 0 (for a block of zeros) and 1 (for a block of non-zeros). Finally, arithmetic coding is applied to both coefficients and residual arrays. At decompression stage, the coefficients matrix is regenerated by scanning the coefficients array and examining the headers to substitute zero and non-zero data. This matrix is then added to the residual array to obtain the original DC values. The IDCT is then applied to obtain the original image. The proposed algorithm has been tested with images of varying sizes in the context of 3D reconstruction. Results demonstrate that our proposed algorithm is superior to traditional JPEG at higher compression ratios with high perceptual quality of images and the ability to reconstruct the 3D models more effectively, both for structured light images and for sequences of multiple viewpoint images.

A novel attention-guided JND Model for improving robust image watermarking

Just noticeable distortion (JND) and visual attention (VA), which are two widely used mathematical models of human visual system (HVS) that aim to simulate the human brain mechanism, are sufficiently explored and applied to many researches including digital watermarking. The activity of human brain, however, is extremely complex and it can be more limited due to complicated fusion of spatial saliency for image domain. In this paper, we propose a novel VA guided JND model in which we fuse the final attention map from the low-level features by using two laws of Gestalt principle. Firstly, we demonstrate a classic JND model in DCT domain, which consists of spatial contrast sensitivity function (CSF), luminance adaptation (LA) and contrast masking (CM). The foveation effect and orientation feature are considered to obtain the CSF and CM factor. The foveation effect is affected by spatial attention, and the orientation features are modeled for CM effect together with traditional block texture strength through three direction-based AC coefficients in DCT domain. The attention features are integrated with a novel Gestalt principle-based weighting mechanism for the final block-based VA model, which is then used to modulate JND profiles with two non-linear functions. Finally, the proposed VA-guided JND model is incorporated into a logarithmic spread transform dither modulation (L-STDM) watermarking scheme. Experimental results show that the newly proposed algorithm can achieve good performance in term of robustness and get better visual quality.

Improved block ordering and frequency selection for reversible data hiding in JPEG images

JPEG images are widely used in daily life. Reversible data hiding (RDH) in JPEG images is useful for many applications. How to avoid image distortion and preserve file size is very important for RDH in JPEG images. In this paper, a novel RDH scheme is proposed for JPEG images based on improved block ordering and frequency selection. The distortion that may be caused by the data embedding in each block is estimated more accurately based on the DCT coefficient distribution, and the blocks that result in less distortion will be firstly used for data embedding. Moreover, the non-zero AC coefficients with the frequencies having higher embedding efficiency will be used preferentially to further reduce image distortion. Consequently, less side information on frequency selection is needed for data extraction and image recovery with our scheme. Extensive experiments are conducted on several typical images and 500 images randomly selected from BOSSbase image database, and the numerical results demonstrate that better performance in terms of visual quality and file size preservation can be achieved with the proposed scheme compared to the state-of-the-art RDH schemes for JPEG images.

A Novel Zero-Watermarking Scheme Based on Variable Parameter Chaotic Mapping in NSPD-DCT Domain

In this paper, a novel image zero-watermarking scheme against rotation attacks is proposed based on nonsubsampled pyramid decomposition (NSPD) and discrete cosine transform (DCT). It utilizes the intrinsic characteristics of NSPD and DCT to extract the robust feature of an image as the original zero-watermark. To increase the security of the proposed scheme, a variable parameter chaotic mapping (VPCM) is designed for the processes of watermark encryption and robust feature extraction. Firstly, the host gray-scale image is decomposed by NSPD, and the low-frequency sub-band image is divided into non-overlapping blocks. After the blocks are transformed by DCT, the signs of the first AC coefficients from all the blocks are used to construct a binary feature image. Then an exclusive-or operation is performed between the binary feature image and the encrypted watermark image to obtain the verification zero-watermark image. Furthermore, a method against arbitrary rotation attacks is employed to improve the robustness of the scheme against geometric attacks. The experimental results demonstrate that the proposed scheme is highly robust against various image processing attacks such as filtering, JPEG compression, scaling, translation, rotation and Checkmark attacks.

Reversible Data Hiding in JPEG Images Based on Negative Influence Models

Reversible data hiding (RDH) can be used to imperceptibly embed data into images in a reversible manner. Many RDH schemes have been developed for uncompressed images. However, JPEG compressed images are more widely used in our daily lives. The existing RDH techniques for JPEG images may cause significant distortion or a large increase in the file size of marked images. In this paper, a novel RDH scheme for JPEG images is proposed. First, the negative influence models of data embedding, including image visual distortion model and file size change model, are mathematically established. Then, a negative index for each frequency is defined as the weighted sum of the normalized average image visual distortion and the normalized average file size change per 1-bit hidden data, and the frequencies with small negative indices will be used for data embedding with a high priority. The weighting factor can be adjusted according to the user’s preference for less image distortion or smaller file size. Lastly, secret data is embedded into non-zero quantized AC coefficients of the selected frequencies in ascending order of zero-run length. Extensive experiments conducted on typical images and a well-known image database show that the presented negative influence models are effective, and the proposed RDH scheme based on the models can achieve low image distortion and small increase in the file size of marked images.

Adaptation of the Clinical Global Impression for Use in Correctional Settings: The CGI-C.

Background: Provision of mental health care in correctional settings presents unique challenges. There is a need for a simple-to-use tool to measure severity of mental illness in correctional settings that can be used by mental health staff from different disciplines. We adapted the severity scale of the Clinical Global Impression for use in correctional settings, which we have called CGI-C, and carried out a reliability study. Method: Clinical descriptions of typical inmate presentations were developed to benchmark each of the seven possible ratings of the CGI. Twenty-one case vignettes were then developed for study of inter-rater reliability, which were then rated using the CGI-C by five forensic psychiatrists (on three occasions) and 11 multidisciplinary health care clinicians (twice). The tool was introduced into clinical practice, and the first 57 joint assessments carried out by both a psychiatrist and a clinician in which a CGI-C was rated were compared to measure inter-rater reliability. Results: We found very good inter-rater and test–retest reliability in all analyses. Gwet’s AC, calculated on initial ratings of the vignettes by the psychiatrists, was 0.85 (95% CI 0.81–0.90, p < 0.001) and 0.87 (95% CI 0.83–0.91, p < 0.001) for clinician ratings. Inter-rater reliability based on 57 joint face-to-face assessments of inmates showed Gwet’s AC coefficient of 0.93 (95% CI 0.88–0.97). Conclusion: The CGI-C is simple to use, can be used by members of the multidisciplinary team, and shows high reliability. The advantage in correctional settings is that it can be used even with the most severely ill and behaviorally disturbed, based on observation and collateral information.

Convolutional Neural Network-Based Arithmetic Coding for HEVC Intra-Predicted Residues

Entropy coding is a fundamental technology in video coding that removes statistical redundancy among syntax elements. In high efficiency video coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the primary entropy coding method. The CABAC consists of three steps: binarization, context modeling, and binary arithmetic coding. As the binarization processes and context models are both manually designed in CABAC, the probability of the syntax elements may not be estimated accurately, which restricts the coding efficiency of CABAC. To address the problem, we propose a convolutional neural network-based arithmetic coding (CNNAC) method and apply it to compress the syntax elements of the intra-predicted residues in HEVC. Instead of manually designing the binarization processes and context models, we propose directly estimating the probability distribution of the syntax elements with a convolutional neural network (CNN), as CNNs can adaptively build complex relationships between inputs and outputs by training with a lot of data. Then, the values of the syntax elements, together with their estimated probability distributions, are fed into a multi-level arithmetic codec to perform entropy coding. In this paper, we have utilized the CNNAC to code the syntax elements of the DC coefficient; the lowest frequency AC coefficient; the second, third, fourth, and fifth lowest frequency AC coefficients; and the position of the last non-zero coefficient in the HEVC intra-predicted residues. The experimental results show that our proposed method achieves up to 6.7% BD-rate reduction and an average of 4.7% BD-rate reduction compared to the HEVC anchor under all intra (AI) configuration.

On the Stability of Reported Pregnancy Intentions from Pregnancy to 1\xa0Year Postnatally: Impact of Choice of Measure, Timing of Assessment, Women\u2019s Characteristics and Outcome of Pregnancy

ObjectivesRetrospective, cross-sectional estimates of pregnancy intention, as used in the Demographic Health Survey (DHS), are the global norm. The London Measure of Unplanned Pregnancy (LMUP) is a newer, psychometrically validated measure which may be more reliable. This paper assesses the reliability of the LMUP and the DHS question over the first postnatal year and explores the effects of maternal characteristics or pregnancy outcome on reported pregnancy intention.MethodsWe compared the test–retest reliability of the LMUP (using the AC coefficient) and DHS question (using the weighted Kappa) over the first postnatal year using data from Malawian women. We investigated the effect of maternal characteristics and pregnancy outcome using t-tests, Chi squared or Fisher’s exact tests, and calculated odds ratios to estimate effect size.ResultsThe DHS question was associated with a statistically significant decrease in the prevalence of unplanned pregnancies from 1-to-12 months postnatally; the LMUP was not. The LMUP had moderate to substantial reliability (0.51–0.66); the DHS had moderate reliability (0.56–0.58). The LMUP’s stability was not related to any of the factors examined; the stability of the DHS varied by marital status (p = 0.033), number of children (p = 0.048) and postnatal depression (p < 0.001). Both underestimated unintended pregnancy postnatally vis-à-vis the LMUP in pregnancy.Conclusions for PracticeThe LMUP is a more reliable measure of pregnancy intention than the DHS in the first postnatal year and does not vary by maternal characteristics or pregnancy outcome. The LMUP should become the gold-standard for measuring pregnancy intention and should be collected in pregnancy or at the first postnatal opportunity.

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for encrypted images in the YUV color space. First, the discrete cosine transform (DCT) is performed on Y component. The resulting DC coefficients are encrypted with stream cipher technology and AC coefficients are encrypted by using value permutation and position scrambling. In addition, U and V components are encrypted using the same encryption method as the AC coefficients. After that, the image owner transmits the encrypted images to the server. Second, the server extracts AC-coefficients histogram from encrypted Y component and two color histograms from encrypted U and V components, and combines these three histograms as image feature. After receiving a query trapdoor from one query user, the cloud server extracts query feature and computes Manhattan distance between query feature and database image feature. Finally, the encrypted images most similar to the query image are returned to the query user.

Minimum entropy and histogram-pair based JPEG image reversible data hiding

In this paper, based on the block entropy in the spatial domain obtained by summing the squares of all AC coefficients of each 8 × 8 block, a reversible data hiding (RDH) scheme for JPEG images is presented which selects the DCT blocks with minimum entropy for RDH. This scheme applies Parseval theorem and entropy principle to achieve the minimum entropy in the spatial domain. It embeds data into the DCT coefficients based on a histogram–pair RDH scheme using histogram shifting. Moreover, four thresholds including the embedding amplitude threshold, the fluctuation threshold, the lower frequency threshold and the higher frequency threshold, are utilized and adjusted to achieve optimized embedding performance. In particular, the proposed scheme does not embed data into zero-valued DCT coefficients, and can obtain optimal PSNR with relatively small file-size increase. Experimental results have demonstrated that, compared with the prior arts, the quality of proposed marked image is higher, and the file-size increase after RDH is less as well.

Hybrid JND model-guided watermarking method for screen content images

With the prevalence of digital products like cellphone, tablet and personal computer, the screen content images (SCIs) consisting of text, graphic, and natural scene picture becomes a significant media in various communication scenarios. In this paper, we propose a novel Spread Transform Dither Modulation (STDM) watermarking scheme based on Hybrid just noticeable distortion model for screen content images. Firstly, the original image was transformed from RGB into YCrCb to ensure stability robustness and invisibility. Then, we proposed a novel automatic classification method based on AC coefficients feature. Different from pictorial block in screen content images, structural-based contrast masking effects was incorporated to adjust the just noticeable distortion value for textual blocks. Finally, the reference image from the SIQAD image database was used to evaluate the performance of our proposed scheme. Experiments showed that our method has a good performance in term of robustness with better visual quality.

Efficient early termination and bypass scheme for CU size decision in spatially scalable SHVC intra coding

As an extension of the High Efficiency Video Coding standard, Scalable High-efficiency Video Coding (SHVC) has been developed to satisfy the challenges posed by heterogeneous environments. A layer-based scheme is employed to support various forms of scalability, but this results in significantly increased computational complexity. A fast Coding Unit (CU) size decision algorithm is proposed to alleviate the additional computational requirement of the Enhancement Layers (ELs). The inter-layer dependency and the spatial correlation are jointly used to skip and early terminate the CU size decision process. In addition, the quantitive relationship between the texture homogeneity and the AC coefficient energy is investigated to further narrow the depth range to be evaluated. Experimental results show that the proposed scheme saves encoding time by up to 72% compared with the SHM 12.0 implementation, with negligible degradation in Rate-Distortion performance.

A robust DCT-2DLDA watermark for color images

A blind watermarking algorithm is proposed, which is based on the Discrete Cosine Transform (DCT) method. It uses Two-Dimensional Linear Discriminant Analysis (2DLDA) watermark scheme for copyright protection. During the embedding process, the color image is converted into the YIQ color space. The quadrature chrominance component is transformed into frequency domain and it uses DCT method. Then, two binary watermarks for reference and logo are added to particular bits of the AC coefficients. During the extraction process, the logo watermark is extracted using matrix-based 2DLDA based on DCT method. By embedding the training data and using a matrix-based 2DLDA scheme, we do not need all the embedded information. The numbers of training samples for each class are small; the training covariance matrices are directly computed from 2D image samples by using 2DLDA. This ensures the efficient and the robustness of watermark extraction. Experimental results demonstrate that the differences between the watermarked image and the original image are indistinguishable. The proposed method is effectively resist common image processing attacks.

A Content-Adaptive Joint Image Compression and Encryption Scheme

For joint image compression and encryption schemes, the encryption power and compression efficiency are commonly two contradictory things. In this paper, we propose a new joint image compression and encryption scheme based on a lossy JPEG standard, which aims at encryption power's enhancement, on the premise of maintaining the JPEG's compression efficiency. The proposed scheme is image-content-adaptive, since the secret encryption key is generated from the plain image using the BLAKE2 hash algorithm. Three encryption operations are contained in our scheme, alternating new orthogonal transforms transformation, dc coefficients encryption, and ac coefficients encryption. To save the cost for transmitting different encryption keys each time to decoder for decryption when the plain image changes, we propose embedding the encryption key into the entropy-encoded bitstream of some ac coefficients, and the whole embedding procedure is controlled by another secret key called the embedding key. Extensive experiments are conducted to show that our encryption scheme is JPEG friendly, and has good confusion and diffusion properties. Detailed security analysis is also given to illustrate the proposed scheme's persistence to various cryptanalysis strategies.

CBIR Using Slant Transform Using DC &amp; AC Coefficients

In the CBIR- (Content Based Image Retrieval) technique requires low-level or primitive features- color, texture, or other data that can be taken from its image Extracting feature vectors of database images as well as query image can be calculated with the help of slant transform by considering DC &amp; 3 AC coefficients obtained in each block of an image. Slant transform represents the gradual brightness changes in an image line effectively. By calculating the difference between feature vector data base and feature vector for a query by using the distance measuring techniques. The vector of the smaller distance is the closest to query image. The experiment is performed in the Corel 500 Image Database. Finally, CBIR results are evaluated by the recall, precision, and F-Score.

Combined Blockiness, Blurriness and White Noise Distortion Meter

Due to discrete cosine transform (DCT) based compression, the loss appears in the form of blocky and blurry distortions in coded pictures. One of the degradation is white noise which corrupts nearly all pixels in the picture. In image transmission system, to assess the image quality at receiver end, lesser image features are sent, instead of the whole reference original picture to accommodate the available bandwidth. The purpose of this research is to propose a procedure which quantifies or estimates the objective picture quality automatically. The presented combined white noise image quality assessment meter (CWNIQAM) functions in the frequency domain for computation of three artefacts blockiness, blurriness and white noise in the corrupted coded images. The designed quality meter at first converts RGB pictures into YCbCr, gray scaled pictures and then edge detection method is applied. Afterward the picture is divided into blocks of 32 × 32 pixels to get local level blockiness, blurriness and white noise value. Next the picture is transformed from spatial to frequency domain and different features called reduced reference parameters are estimated. The composite magnitudes strength through horizontal and vertical harmonics is obtained for estimation of blocky and blurry artefacts in the coded images, while the strength of all ac coefficients and dc component is obtained for computation of white noise. The reduced reference features are computed and compared for reference and coded picture for estimating the particular type of distortion. The uniqueness of the proposed CWNIQAM meter is that, it can estimate the quality of corrupted images by estimating the combination of blockiness, blurriness and white noise through this single meter. The LIVE database2 having 174 different white noise (wn) corrupted images are used for measuring combined objective value of the three artefacts using the designed reduced reference image quality assessment meter algorithm. The objective results are correlated with subjective wn, DMOS LIVE scores. The correlation of about 98% is attained which indicate that much better picture quality index is achieved by the offered reduced reference combined white noise image quality evaluation meter in the frequency domain.

Revealing the traces of histogram equalisation in digital images

The popular histogram equalisation (HE) technique, which was developed to improve the image contrast, sometimes may also be misused to hide intensity variations in tampered images with ill intention. The authors investigate how existing image forensic techniques may fail to detect HE operation in highly compressed and low-resolution images. They then propose an algorithm to detect whether a given image (either uncompressed or JPEG compressed) has undergone the HE process or not. It is based on the frequency domain analysis of image histogram and exploits the difference in DC and AC coefficients in histogram's discrete Fourier transform. It can detect HE operation even if the image is saved in JPEG format after the equalisation, where most the existing algorithms fail. The extensive computer simulations over large dataset show the effectiveness of the proposed algorithm.

In-loop perceptual model-based rate-distortion optimization for HEVC real-time encoder

In this paper, a novel High Efficiency Video Coding (HEVC)-compliant perceptual rate-distortion optimization (RDO) scheme is proposed based on motion attention and visual distortion sensitivity models, which both fully utilize in-loop coding information of HEVC. In detail, the motion attention model is designed by using the motion vectors (MVs) estimated during the inter-prediction process. The MV field is refined based on maximum a posteriori (MAP) estimation to remove MV outliers and improve the model’s efficiency. In addition, the visual distortion sensitivity is modeled by using the spatiotemporal energy of AC coefficients, which are obtained from HEVC transform process. Then, these two models are incorporated together into the RDO process. As a result, the Lagrange multiplier and quantization parameter are adjusted adaptively in an analytical way. Since the two models are calculated within the HEVC coding loop, the complexity increase is limited. The experimental results indicate that the proposed perceptual RDO scheme can achieve significantly better rate-VQM performance than the conventional RDO scheme. Specifically, the BD-rate can reach a maximum 24.45% and an average 13.68% reduction in terms of the Bjontegaard Delta metric compared to HEVC practical encoder x265.

Efficient Encryption Technique for H.264/AVC Videos Based on CABAC and Logistic Map

Nowadays, the demands of real-time video communication are increased rapidly. Search and rescue(SAR) applications like earthquake rescue, avalanche victims, wildfire monitoring in addition to highway surveillance are considered examples of real-time applications. In which, communication time is considered the most important metric to be optimized to ensure support for victims’ lives. Thus finding a simple and time efficient encryption technique for securing the transmitted data become mandatory. In this paper, we present an efficient encryption technique which has low computation complexity, low processing time and highly chaotic encrypted videos. The proposed technique is based on CABAC where the bin-string of Intra-Prediction Mode is encrypted with chaotic signals and the sign of MVD is toggled randomly. For residue coefficients the sign of the AC coefficients are flipped randomly and the first value of DC coefficients is encrypted by XORing the bin-string with random stream. All random streams are generated with chaotic systems using Logistic map. The experimental results shows that the proposed technique is highly effective for real-time application and robust against different &lt;br /&gt; types of attacks.

Efficient Steganography Algorithm Based On DCT And Entropy Thresholding Technique

Steganography is the special art of hidding important and confidential information in appropriate multimedia carrier. It also restrict the detection of hidden messages. In this paper we proposes steganographic method based on dct and entropy thresholding technique. The steganographic algorithm uses random function in order to select block of the image where the elements of the binary sequence of a secret message will be inserted. Insertion takes place at the lower frequency AC coefficients of the block. Before we insert the secret message. Image under goes dc transformations after insertion of the secret message we apply inverse dc transformations. Secret message will only be inserted into a particular block if entropy value of that particular block is greater then threshold value of the entropy and if block is selected by the random function. In Experimental work we calculated the peak signal to noise ratio(PSNR), Absolute difference , Relative entropy. Proposed algorithm give high value of PSNR and low value of Absolute difference which clearly indicate level of distortion in image due to insertion of secret message is reduced. Also value of relative entropy is close to zero which clearly indicate proposed algorithm is sufficiently secure.