Showing papers on "Discrete cosine transform published in 2018"

A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine

Abstract: In this paper, an algorithm for multiple watermarking based on discrete wavelet transforms (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) has been proposed for healthcare applications. For identity authentication purpose, the proposed method uses three watermarks in the form of medical Lump image watermark, the doctor signature/identification code and diagnostic information of the patient as the text watermarks. In order to improve the robustness performance of the image watermark, Back Propagation Neural Network (BPNN) is applied to the extracted image watermark to reduce the noise effects on the watermarked image. The security of the image watermark is also enhanced by using Arnold transform before embedding into the cover. Further, the symptom and signature text watermarks are also encoded by lossless arithmetic compression technique and Hamming error correction code respectively. The compressed and encoded text watermark is then embedded into the cover image. Experimental results are obtained by varying the gain factor, different sizes of text watermarks and the different cover image modalities. The results are provided to illustrate that the proposed method is able to withstand a different of signal processing attacks and has been found to be giving excellent performance for robustness, imperceptibility, capacity and security simultaneously. The robustness performance of the method is also compared with other reported techniques. Finally, the visual quality of the watermarked image is evaluated by the subjective method also. This shows that the visual quality of the watermarked images is acceptable for diagnosis at different gain factors. Therefore the proposed method may find potential application in prevention of patient identity theft in healthcare applications.

Robust color image watermarking technique in the spatial domain

Abstract: This paper proposes a new blind watermarking algorithm, which embedding the binary watermark into the blue component of a RGB image in the spatial domain, to resolve the problem of protecting copyright. For embedding watermark, the generation principle and distribution features of direct current (DC) coefficient are used to directly modify the pixel values in the spatial domain, and then four different sub-watermarks are embedded into the different areas of the host image for four times, respectively. When watermark extraction, the sub-watermark is extracted with blind manner according to DC coefficients of watermarked image and the key-based quantization step, and then the statistical rule and the method of “first to select, second to combine” are proposed to form the final watermark. Hence, the proposed algorithm is executed in the spatial domain rather than in discrete cosine transform (DCT) domain, which not only has simple and quick performance of the spatial domain but also has high robustness feature of DCT domain. The experimental results show that the proposed watermarking algorithm can obtain better invisibility of watermark and stronger robustness for common attacks, e.g., JPEG compression, cropping, and adding noise. Comparison results also show the advantages of the proposed method.

Secure and Robust Digital Image Watermarking Using Coefficient Differencing and Chaotic Encryption

Abstract: This paper presents a chaotic encryption-based blind digital image watermarking technique applicable to both grayscale and color images. Discrete cosine transform (DCT) is used before embedding the watermark in the host image. The host image is divided into $8\times 8$ nonoverlapping blocks prior to DCT application, and the watermark bit is embedded by modifying difference between DCT coefficients of adjacent blocks. Arnold transform is used in addition to chaotic encryption to add double-layer security to the watermark. Three different variants of the proposed algorithm have been tested and analyzed. The simulation results show that the proposed scheme is robust to most of the image processing operations like joint picture expert group compression, sharpening, cropping, and median filtering. To validate the efficiency of the proposed technique, the simulation results are compared with certain state-of-art techniques. The comparison results illustrate that the proposed scheme performs better in terms of robustness, security, and imperceptivity. Given the merits of the proposed scheme, it can be used in applications like e-healthcare and telemedicine to robustly hide electronic health records in medical images.

Faster Neural Networks Straight from JPEG

Abstract: The simple, elegant approach of training convolutional neural networks (CNNs) directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. Intuitively, when processing JPEG images using CNNs, it seems unnecessary to decompress a blockwise frequency representation to an expanded pixel representation, shuffle it from CPU to GPU, and then process it with a CNN that will learn something similar to a transform back to frequency representation in its first layers. Why not skip both steps and feed the frequency domain into the network directly? In this paper we modify \libjpeg to produce DCT coefficients directly, modify a ResNet-50 network to accommodate the differently sized and strided input, and evaluate performance on ImageNet. We find networks that are both faster and more accurate, as well as networks with about the same accuracy but 1.77x faster than ResNet-50.

Robust Coverless Image Steganography Based on DCT and LDA Topic Classification

Abstract: In order to improve the robustness and capability of resisting image steganalysis, a novel coverless image steganography algorithm based on discrete cosine transform and latent dirichlet allocation (LDA) topic classification is proposed. First, latent dirichlet allocation topic model is utilized for classifying the image database. Second, the images belonging to one topic are selected, and 8 × 8 block discrete cosine transform is performed to these images. Then robust feature sequence is generated through the relation between direct current coefficients in the adjacent blocks. Finally, an inverted index which contains the feature sequence, dc , location coordinates, and image path is created. For the purpose of achieving image steganography, the secret information is converted into a binary sequence and partitioned into segments, and the image whose feature sequence equals to the secret information segments is chosen as the cover image according to the index. After that, all cover images are sent to the receiver. In the whole process, no modification is done to the original images. Experimental results and analysis show that the proposed algorithm can resist the detection of existing steganalysis algorithms, and has better robustness against common image processing and better ability to resist steganalysis compared with the existing coverless image steganography algorithms. Meanwhile, it is resistant to geometric attacks to some extent. It has great potential application in secure communication of big data environment.

Dmcnn: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal

Abstract: JPEG is one of the most commonly used standards among lossy image compression methods. However, JPEG compression inevitably introduces various kinds of artifacts, especially at high compression rates, which could greatly affect the Quality of Experience (QoE). Recently, convolutional neural network (CNN) based methods have shown excellent performance for removing the JPEG artifacts. Lots of efforts have been made to deepen the CNN s and extract deeper features, while relatively few works pay attention to the receptive field of the network. In this paper, we illustrate that the quality of output images can be significantly improved by enlarging the receptive fields in many cases. One step further, we propose a Dual-domain Multi-scale CNN (DMCNN) to take full advantage of redundancies on both the pixel and DCT domains. Experiments show that DMCNN sets a new state-of-the-art for the task of JPEG artifact removal.

Detection of copy-move image forgery based on discrete cosine transform

Abstract: Since powerful editing software is easily accessible, manipulation on images is expedient and easy without leaving any noticeable evidences. Hence, it turns out to be a challenging chore to authenticate the genuineness of images as it is impossible for human's naked eye to distinguish between the tampered image and actual image. Among the most common methods extensively used to copy and paste regions within the same image in tampering image is the copy-move method. Discrete Cosine Transform (DCT) has the ability to detect tampered regions accurately. Nevertheless, in terms of precision (FP) and recall (FN), the block size of overlapping block influenced the performance. In this paper, the researchers implemented the copy-move image forgery detection using DCT coefficient. Firstly, by using the standard image conversion technique, RGB image is transformed into grayscale image. Consequently, grayscale image is segregated into overlying blocks of m × m pixels, m = 4.8. 2D DCT coefficients are calculated and reposition into a feature vector using zig-zag scanning in every block. Eventually, lexicographic sort is used to sort the feature vectors. Finally, the duplicated block is located by the Euclidean Distance. In order to gauge the performance of the copy-move detection techniques with various block sizes with respect to accuracy and storage, threshold D_similar = 0.1 and distance threshold (N)_d = 100 are used to implement the 10 input images in order. Consequently, 4 × 4 overlying block size had high false positive thus decreased the accuracy of forged detection in terms of accuracy. However, 8 × 8 overlying block accomplished more accurately for forged detection in terms of precision and recall as compared to 4 × 4 overlying block. In a nutshell, the result of the accuracy performance of different overlying block size are influenced by the diverse size of forged area, distance between two forged areas and threshold value used for the research.

A Robust Image Watermarking Technique With an Optimal DCT-Psychovisual Threshold

Abstract: This paper presents a reliable digital watermarking technique that provides high imperceptibility and robustness for copyright protection using an optimal discrete cosine transform (DCT) psychovisual threshold. An embedding process in this watermarking technique utilizes certain frequency regions of DCT, such that insertion of watermark bits causes the least image distortion. Thus, the optimal psychovisual threshold is determined to embed the watermark in the host image for the best image quality. During the insertion of watermark bits into the certain frequencies of the image, watermark bits are not directly inserted into the frequency coefficient; rather, the certain coefficients are modified based on some rules to construct the watermarked image. The embedding frequencies are determined by using modified entropy finding large redundant areas. Furthermore, the watermark is scrambled before embedding to provide an additional security. In order to verify the proposed technique, our technique is tested under several signal processing and geometric attacks. The experimental results show that our technique achieves higher invisibility and robustness than the existing schemes. The watermark extraction produces high image quality after different types of attacks.

High-Agreement Uncorrelated Secret Key Generation Based on Principal Component Analysis Preprocessing

Abstract: Random and high-agreement secret key generation from noisy wideband channels is challenging due to the autocorrelation inside the channel samples and compromised cross correlation between channel measurements of two keying parties. This paper studies the signal preprocessing algorithms to establish high-agreement uncorrelated secret key in the presence of channel independent eavesdroppers. We first propose a general mathematical model for various preprocessing schemes, including principal component analysis (PCA), discrete cosine transform (DCT) and wavelet transform (WT). Among preprocessing schemes, PCA is proved to achieve the optimal secret key rate. Next, PCA with common eigenvector has been found to outperform PCA with private eigenvector in terms of an overall consideration of key agreement, information leakage, and computational expense. Then, we propose a system level design of key generation, including quantization, information reconciliation, and privacy amplification. Numerical results verify that the key generation enhanced by PCA with common eigenvector can achieve secret key with high key generation rate, low key error rate, and good randomness.

Histopathological breast-image classification using local and frequency domains by convolutional neural network

Abstract: Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200× dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively.

Fast Blind Instrument Function Estimation Method for Industrial Infrared Spectrometers

Abstract: Infrared (IR) spectrometers, particularly the aging ones, often suffer from the band overlap and random noise. In this paper, a blind estimation method based on discrete cosine transform (DCT) regularization is proposed for IR spectrum measured from an aging spectrometer instrument. Motivated by the observation that the DCT coefficient distribution of the ground-truth spectrum is sparser than that of the observed spectrum, an IR spectral deconvolution model is formulated in our method to regularize the distribution of the observed spectrum by total variation regularization. Then, the split Bregman method is exploited to solve the resulting optimization problem. The experimental results demonstrate an encouraging performance of the proposed approach to suppress noise and preserve spectral details. The novelty of our method lies on its ability to estimate instrument function and latent spectrum in a joint framework; thus, mitigating the effects of instrument aging to a large extent. The recovered IR spectra can efficiently capture the spectral features and interpret the unknown chemical mixture in industrial applications.

Facial expression recognition using a combination of multiple facial features and support vector machine

Abstract: This paper presents a novel facial expression recognition (FER) technique based on support vector machine (SVM) for the FER Here it is called the FERS technique First, the FERS technique develops a face detection method that combines the Haar-like features method with the self-quotient image (SQI) filter As a result, the FERS technique possesses better detection rate because the face detection method gets more accurate in locating face regions of an image The main reason is that the SQI filter can overcome the insufficient light and shade light Subsequently, three schemes, the angular radial transform (ART), the discrete cosine transform (DCT) and the Gabor filter (GF), are simultaneously employed in the design of the feature extraction for facial expression in the FERS technique More specifically, they are employed in constructing a set of training patterns for the training of an SVM The FERS technique then exploits the trained SVM to recognize the facial expression for a query face image Finally, experimental results show that the recognition performance of the FERS technique can be better than that of other existing methods under consideration in the paper

A novel hybrid of DCT and SVD in DWT domain for robust and invisible blind image watermarking with optimal embedding strength

Abstract: To optimize the tradeoff between imperceptibility and robustness properties, this paper proposes a robust and invisible blind image watermarking scheme based on a new combination of discrete cosine transform (DCT) and singular value decomposition (SVD) in discrete wavelet transform (DWT) domain using least-square curve fitting and logistic chaotic map. Firstly cover image is decomposed into four subbands using DWT and the low frequency subband LL is partitioned into non-overlapping blocks. Then DCT is applied to each block and several particular middle frequency DCT coefficients are extracted to form a modulation matrix, which is used to embed watermark signal by modifying its largest singular values in SVD domain. Optimal embedding strength for a specific cover image is obtained from an estimation based on least-square curve fitting and provides a good compromise between transparency and robustness of watermarking scheme. The security of the watermarking scheme is ensured by logistic chaotic map. Experimental results demonstrate the better effectiveness of the proposed watermarking scheme in the perceptual quality and the ability of resisting to conventional signal processing and geometric attacks, in comparison with the related existing methods.

Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

Abstract: Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Abstract: Blind watermarking targets the challenging recovery of the watermark when the host is not available during the detection stage. This paper proposes Discrete Shearlet Transform (DST) as a new embedding domain for blind image watermarking. Our novel DST blind watermark detection system uses a nonadditive scheme based on the statistical decision theory. It first computes the Probability Density Function (PDF) of the DST coefficients modeled as a Laplacian distribution. The resulting likelihood ratio is compared with a decision threshold calculated using Neyman–Pearson criterion to minimize the missed detection subject to a fixed false alarm probability. Our method is evaluated in terms of imperceptibility, robustness, and payload against different attacks (Gaussian noise, blurring, cropping, compression, and rotation) using 30 standard grayscale images covering different characteristics (smooth, more complex with a lot of edges, and high detail textured regions). The proposed method shows greater windowing flexibility with more sensitive to directional and anisotropic features when compared against discrete wavelet and contourlets.

Hybrid blind robust image watermarking technique based on DFT-DCT and Arnold transform

Abstract: In this paper, a robust blind image watermarking method is proposed for copyright protection of digital images. This hybrid method relies on combining two well-known transforms that are the discrete Fourier transform (DFT) and the discrete cosine transform (DCT). The motivation behind this combination is to enhance the imperceptibility and the robustness. The imperceptibility requirement is achieved by using magnitudes of DFT coefficients while the robustness improvement is ensured by applying DCT to the DFT coefficients magnitude. The watermark is embedded by modifying the coefficients of the middle band of the DCT using a secret key. The security of the proposed method is enhanced by applying Arnold transform (AT) to the watermark before embedding. Experiments were conducted on natural and textured images. Results show that, compared with state-of-the-art methods, the proposed method is robust to a wide range of attacks while preserving high imperceptibility.

Joint Separable and Non-Separable Transforms for Next-Generation Video Coding

Abstract: Throughout the past few decades, the separable discrete cosine transform (DCT), particularly the DCT type II, has been widely used in image and video compression. It is well-known that, under first-order stationary Markov conditions, DCT is an efficient approximation of the optimal Karhunen–Loeve transform. However, for natural image and video sources, the adaptivity of a single separable transform with fixed core is rather limited for the highly dynamic image statistics, e.g., textures and arbitrarily directed edges. It is also known that non-separable transforms can achieve better compression efficiency for images with directional texture patterns, yet they are computationally complex, especially when the transform size is large. In order to achieve higher transform coding gains with relatively low-complexity implementations, we propose a joint separable and non-separable transform. The proposed separable primary transform, named enhanced multiple transform (EMT), applies multiple transform cores from a pre-defined subset of sinusoidal transforms, and the transform selection is signaled in a joint block level manner. Moreover, a non-separable secondary transform (NSST) method is proposed to operate in conjunction with EMT. Unlike the existing non-separable transform schemes which require excessive amounts of memory and computation, the proposed NSST efficiently improves coding gain with much lower complexity. Extensive experimental results show that the proposed methods, in a state-of-the-art video codec, such as high efficiency video coding, can provide significant coding gains (average 6.9% and 4.5% bitrate reductions for intra and random-access coding, respectively).

A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

Abstract: One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT.

Approximate DCT Image Compression Using Inexact Computing

Abstract: This paper proposes a new framework for digital image processing; it relies on inexact computing to address some of the challenges associated with the discrete cosine transform (DCT) compression. The proposed framework has three levels of processing; the first level uses approximate DCT for image compressing to eliminate all computational intensive floating-point multiplications and executing the DCT processing by integer additions and in some cases logical right/left shifts. The second level further reduces the amount of data (from the first level) that need to be processed by filtering those frequencies that cannot be detected by human senses. Finally, to reduce power consumption and delay, the third level introduces circuit level inexact adders to compute the DCT. For assessment, a set of standardized images are compressed using the proposed three-level framework. Different figures of merits (such as energy consumption, delay, power-signal-to-noise-ratio, average-difference, and absolute-maximum-difference) are compared to existing compression methods; an error analysis is also pursued confirming the simulation results. Results show very good improvements in reduction for energy and delay, while maintaining acceptable accuracy levels for image processing applications.

A 4D DCT-Based Lenslet Light Field Codec

Abstract: Light fields aim to represent visual information in 3D space They are 4D structures that contain the images of a given scene from a sampled 2D range of viewpoints When acquired using a lenslet camera, in addition to the ordinary intra-view redundancy, these views have a great deal of inter-view redundancy In this work we propose a light field codec that fully exploits the 4D redundancy of light fields by using a 4D transform and hexadeca-trees It initially divides the light field into 4D blocks and computes a 4D Discrete Cosine Transform of each one Then the transform coefficients of the 4D block are grouped using hexadeca-trees on a bitplane-by-bitplane basis, and the generated stream is encoded using an adaptive arithmetic coder The proposed codec has been employed to encode the JPEG Pleno lenslet light fields The rate-distortion results have been assessed using test conditions comparable to the ones presented at the ICIP 2017 Light Field Coding Grand Challenge The proposed codec, despite being conceptually simple, achieves competitive rate-distortion performance

Approximate Reverse Carry Propagate Adder for Energy-Efficient DSP Applications

Abstract: In this paper, a reverse carry propagate adder (RCPA) is presented. In the RCPA structure, the carry signal propagates in a counter-flow manner from the most significant bit to the least significant bit; hence, the carry input signal has higher significance than the output carry. This method of carry propagation leads to higher stability in the presence of delay variations. Three implementations of the reverse carry propagate full-adder (RCPFA) cell with different delay, power, energy, and accuracy levels are introduced. The proposed structure may be combined with an exact (forward) carry adder to form hybrid adders with tunable levels of accuracy. The design parameters of the proposed RCPA implementations and some hybrid adders realized utilizing these structures are studied and compared with those of the state-of-the-art approximate adders using HSPICE simulations in a 45-nm CMOS technology. The results indicate that employing the proposed RCPAs in the hybrid adders may provide, on average, 27%, 6%, and 31% improvements in delay, energy, and energy-delay-product while providing higher levels of accuracy. In addition, the structure is more resilient to delay variation compared to the conventional approximate adder. Finally, the efficacy of the proposed RCPAs is investigated in the discrete cosine transform (DCT) block of the JPEG compression and finite-impulse response (FIR) filter applications. The investigation reveals 60% and 39% energy saving in the DCT of JPEG and FIR filter, respectively, for the proposed RCPAs.

Reliable facial expression recognition for multi-scale images using weber local binary image based cosine transform features

Abstract: Accurate recognition of facial expression is a challenging problem especially from multi-scale and multi orientation face images. In this article, we propose a novel technique called Weber Local Binary Image Cosine Transform (WLBI-CT). WLBI-CT extracts and integrates the frequency components of images obtained through Weber local descriptor and local binary descriptor. These frequency components help in accurate classification of various facial expressions in the challenging domain of multi-scale and multi-orientation facial images. Identification of significant feature set plays a vital role in the success of any facial expression recognition system. Effect of multiple feature sets with varying block sizes has been investigated using different multi-scale images taken from well-known JAFEE, MMI and CK+ datasets. Extensive experimentation has been performed to demonstrate that the proposed technique outperforms the contemporary techniques in terms of recognition rate and computational time.

Learning-Based Just-Noticeable-Quantization- Distortion Modeling for Perceptual Video Coding

Abstract: Conventional predictive video coding-based approaches are reaching the limit of their potential coding efficiency improvements, because of severely increasing computation complexity. As an alternative approach, perceptual video coding (PVC) has attempted to achieve high coding efficiency by eliminating perceptual redundancy, using just-noticeable-distortion (JND) directed PVC. The previous JNDs were modeled by adding white Gaussian noise or specific signal patterns into the original images, which were not appropriate in finding JND thresholds due to distortion with energy reduction. In this paper, we present a novel discrete cosine transform-based energy-reduced JND model, called ERJND, that is more suitable for JND-based PVC schemes. Then, the proposed ERJND model is extended to two learning-based just-noticeable-quantization-distortion (JNQD) models as preprocessing that can be applied for perceptual video coding. The two JNQD models can automatically adjust JND levels based on given quantization step sizes. One of the two JNQD models, called LR-JNQD, is based on linear regression and determines the model parameter for JNQD based on extracted handcraft features. The other JNQD model is based on a convolution neural network (CNN), called CNN-JNQD. To our best knowledge, our paper is the first approach to automatically adjust JND levels according to quantization step sizes for preprocessing the input to video encoders. In experiments, both the LR-JNQD and CNN-JNQD models were applied to high efficiency video coding (HEVC) and yielded maximum (average) bitrate reductions of 38.51% (10.38%) and 67.88% (24.91%), respectively, with little subjective video quality degradation, compared with the input without preprocessing applied.

On Improving Distortion Functions for JPEG Steganography

Abstract: This paper proposes a framework to improve the existing distortion functions designed for JPEG steganography, which results in a better capability of countering steganalysis. Different from the existing steganography approach that minimizes image distortion, we minimize the feature distortion caused by data embedding. Given a JPEG image, we construct a reference image close to the image before JPEG compression. Guided by both the reference image and the feature distortion minimization, the state-of-the-art distortion functions designed for syndrome trellis coding embedding are improved by distinguishing the embedding costs for +1 versus −1 embedding. This paper has three contributions. First, the proposed framework outperforms the traditional, since we use the constructed reference image and the public steganalytic knowledge for data embedding. Second, the proposed framework is universal for improving distortion functions that were designed for JPEG steganography. Finally, experimental results also prove that the proposed approach has a better undetectability when examined by modern steganalytic tools.

Efficient Encrypted Images Filtering and Transform Coding With Walsh-Hadamard Transform and Parallelization

Abstract: Since homomorphic encryption operations have high computational complexity, image applications based on homomorphic encryption are often time consuming, which makes them impractical. In this paper, we study efficient encrypted image applications with the encrypted domain Walsh-Hadamard transform (WHT) and parallel algorithms. We first present methods to implement real and complex WHTs in the encrypted domain. We then propose a parallel algorithm to improve the computational efficiency of the encrypted domain WHT. To compare the WHT with the discrete cosine transform (DCT), integer DCT, and Haar transform in the encrypted domain, we conduct theoretical analysis and experimental verification, which reveal that the encrypted domain WHT has the advantages of lower computational complexity and a shorter running time. Our analysis shows that the encrypted WHT can accommodate plaintext data of larger values and has better energy compaction ability on dithered images. We propose two encrypted image applications using the encrypted domain WHT. To accelerate the practical execution, we present two parallelization strategies for the proposed applications. The experimental results show that the speedup of the homomorphic encrypted image application exceeds 12.

A Simple Yet Efficient Accuracy-Configurable Adder Design

Abstract: Approximate computing is a promising approach for low-power IC design and has recently received considerable research attention. To accommodate dynamic levels of approximation, a few accuracy-configurable adder (ACA) designs have been developed in the past. However, these designs tend to incur large area overheads as they rely on either redundant computing or complicated carry prediction. Some of these designs include error detection and correction circuitry, which further increase the area. In this paper, we investigate a simple ACA design that contains no redundancy or error detection/correction circuitry and uses very simple carry prediction. The simulation results show that our design dominates the latest previous work on accuracy-delay-power tradeoff while using 39% lower area. In the best case, the iso-delay power of our design is only 16% of accurate adder regardless of degradation in accuracy. One variant of this design provides finer-grained and larger tunability than that of the previous works. Moreover, we propose a delay-adaptive self-configuration technique to further improve the accuracy-delay-power tradeoff. The advantages of our method are confirmed by the applications in multiplication and discrete cosine transform computing.