Quantization Matrix Research Articles

A Low-Power Speech Recognizer and Voice Activity Detector Using Deep Neural Networks

This paper describes digital circuit architectures for automatic speech recognition (ASR) and voice activity detection (VAD) with improved accuracy, programmability, and scalability. Our ASR architecture is designed to minimize off-chip memory bandwidth, which is the main driver of system power consumption. A SIMD processor with 32 parallel execution units efficiently evaluates feed-forward deep neural networks (NNs) for ASR, limiting memory usage with a sparse quantized weight matrix format. We argue that VADs should prioritize accuracy over area and power, and introduce a VAD circuit that uses an NN to classify modulation frequency features with 22.3- $\mu \text{W}$ power consumption. The 65-nm test chip is shown to perform a variety of ASR tasks in real time, with vocabularies ranging from 11 words to 145 000 words and full-chip power consumption ranging from 172 $\mu \text{W}$ to 7.78 mW.

Podejście projektowe oparte na korelacyjnym związku między konserwowalnością a funkcjonalną budową produktu

As an important quality characteristic, maintainability is the ability of a product to be repaired efficiently and economically. Because it is mainly determined at the design stage, maintainability is mostly affected by the construction of a product. Traditional product design methods put more focus on design for function and production, neglecting design for maintainability, which causes a gap between functional construction design and maintainability design. The delay of maintainability design results in huge costs for design changes and even irrevocable design flaws. Because of the weak relationship between functional construction and maintainability in product design, the influence of maintainability design on the product is limited. To resolve this problem, this paper proposes a design approach considering the relationship between maintainability and functional construction. First, maintainability design factors (MDFs) and functional construction design factors (FCDFs) are defined and classified. Second, based on topology graphic theory, a correlative relationship model is constructed by graphically combining the MDFs and FCDFs into a network diagram. Third, to determine primary design factors, a quantization matrix is developed to perform importance evaluation of the correlative relationship. Finally, a practical case is studied by implementing the proposed approach for the lubrication system of an armoured vehicle. The results validate the effectiveness and feasibility of the approach.

High efficient video coding using weighted entropy with optimized quantization matrix

As the concept of quantization matrix becomes an important feature in recent video CODECs, an optimized quantization matrix is being considered in the High-Efficiency Video Coding (HEVC) standard. This paper describes the entropy encoding by familiarizing optimized quantization matrix, and so higher rate of compression can be accomplished over the improved entropy encoding. Experiments show that for the eight benchmark video sequences and PSNR for varying rate of data transmission is explored. Comparative analysis is made with the improved (WE-Encoding) and standard entropy encoding based on the performance measurements. The simulation results show that the proposed method (WE-OQM) can save the originality of the decoded video sequence far better even though the compression rate is increased. In addition, the overall analysis states that the proposed method is 35.29% better than the Standard Encoding and 62.5% better than the WE-Encoding.

Robust first quantization matrix estimation based on filtering of recompression artifacts for non-aligned double compressed JPEG images

This paper presents a novel method for first quantization matrix estimation for non-aligned double JPEG compressed images. The non-aligned double JPEG scenario poses several challenges in the form of recompression artifacts which needs to be handled effectively in order to arrive at accurate first quantization estimate. One such recompression artifact which is prominent in the non-aligned scenario is the blocking artifacts arising due to the misalignment of the DCT grids in the successive compression cycles. The proposed method investigates the impact of the blocking artifacts induced errors on the DCT histogram and counters these errors via a novel DCT histogram filtering strategy. In addition, the residual noise which is also a recompression artifact is countered utilizing the local rank transform which adaptively filters the residual noise effects. The filtered histograms are compared with a synthetically created ideal second quantization matrix to estimate the degree of similarity between the two cases. Utilizing the maximum degree of similarity, the selection of first quantization value is performed. Experimental analysis utilizing several non-aligned double compressed JPEG images taken from UCID and RAISE datasets with Quality Factor (QF) of second compression being greater than the first compression (QF2>QF1) along with the comparative analysis with one of the state-of-the-art method shows accurate estimation of the first quantization values for the proposed method. The proposed method finds its application in image forensics as well as in steganalysis.

Intelligent Watermarking Scheme for image Authentication and Recovery

Recently, researchers have proposed semi-fragile watermarking techniques with the additional capability of image recovery. However, these approaches have certain limitations with respect to capacity, imperceptibility, and robustness. In this paper, we are proposing two independent watermarks, one for image recovery and the other for authentication. The first watermark (image digest), a highly compressed version of the original image itself, is used to recover the distorted image. Unlike the traditional quantisation matrix, genetic programming based matrices are used for compression purposes. These matrices are based on the local characteristics of the original image. Furthermore, a second watermark, which is a pseudo-random binary matrix, is generated to authenticate the host image precisely. Experimental results show that the semi-fragility of the watermarks makes the proposed scheme tolerant of JPEG lossy compression and it locates the tampered regions accurately.

Transform-based coding method for remote sensing hyperspectral data compression

A version of the transform-based coding compression method for remote sensing hyperspectral data is proposed. New version is based on the calculation of a separate quantization matrix for each image and the preliminary removal of low-frequency components in the input data. Larger compression ratios are shown in comparison with the layered JPEG algorithm with the same mean square recovery error.

An Efficient Image Watermarking Method Based on Fast Discrete Cosine Transform Algorithm

This paper proposes an image watermarking method based on the fast discrete cosine transform (DCT) algorithm for implementation in digital signal processor. A digital watermark can be effectively embedded and efficiently extracted without the host image. The keys in watermarking process include four frequency coefficients in DCT, two random permutation vectors, and a quantization matrix for normalizing the watermark and the host image. The fast DCT algorithm has been shown to reduce the complexity of two-dimensional image transformation so that embedding/decoding an image watermark can be completed in real time within 0.33 seconds. The quality of both watermarked image and extracted (retrieved) watermark remains excellent. It is shown that the watermarking method is efficient in and robust to data cropping, transmission loss, and compression/decompression.

Design Analysis Method for Multidisciplinary Complex Product using SysML

In the design of multidisciplinary complex products, model-based systems engineering methods are widely used. However, the methodologies only contain only modeling order and simple analysis steps, and lack integrated design analysis methods supporting the whole process. In order to solve the problem, a conceptual design analysis method with integrating modern design methods has been proposed. First, based on the requirement analysis of the quantization matrix, the user’s needs are quantitatively evaluated and translated to system requirements. Then, by the function decomposition of the function knowledge base, the total function is semi-automatically decomposed into the predefined atomic function. The function is matched into predefined structure through the behaviour layer using function-structure mapping based on the interface matching. Finally based on design structure matrix (DSM), the structure reorganization is completed. The process of analysis is implemented with SysML, and illustrated through an aircraft air conditioning system for the system validation.

Perception-based adaptive quantization for transform-domain Wyner-Ziv video coding

Distributed video coding (DVC) is desirable for encoding systems with tight power or computational constraints, for which the popular practical solution is transform-domain Wyner-Ziv video coding (TD-WZVC). To achieve the similar coding performance with H.264/AVC, quantization is a key factor in TD-WZVC. Practically, the quantization matrix is trained offline and remains fixed value during coding. Optimal rate-distortion (RD) performance cannot be achieved due to the varying quality of side information (SI) frame. In this paper, a novel model of perceptual distortion probability is developed to estimate the perceptual distortion of SI frame and to derive the target perceptual distortion. With the two perceptual distortion probabilities, three components (i.e. quality of SI frame, perceptual features and RD optimization) are integrated to determine the optimal quantization matrix adaptively, which improves the coding performance. Extensive experiments demonstrate that the proposed scheme can adaptively determine proper quantization matrix online and achieve similar visual quality with less bit-rate, as compared to other adaptive quantization schemes in TD-WZVC.

Artifact Reduction For Visually Challenged

Compression of image or video now happens to be an essential feature to fit a large amount of binary data into an available narrow bandwidth digital communication channel. Inveterate compression algorithms formulated by means of Discrete Cosine Transform and Wavelet Transform suffers a lot with the presence of undesirable frequency components which in turn pull down the visual quality of the image or video. Those frequency components create prominent visible changes, identified as artifacts. The visual quality of such images needs improvement by the elimination of these artifacts not only for consumer electronics but also for the analysis and decision making algorithms in real time systems. Artifacts can be modeled with the characteristics of neighboring pixels. The most noticeable are blocking, ringing and mosquito artifacts. The proposed adaptive filter applicable for JPEG compression algorithms detects the blocking artifacts with the new metric voted as Total Blocking Error. Subsequently the adaptive control superimposed for the corrective parameter of the localized filter and for the correlation factor to create the new adaptive quantization matrix for correcting the rounded off Discrete Cosine Transform coefficients at the transmitter side reduces the blocking artifacts. The boundary pixels of each n*n block yields the measure of blocking annoyances with this new metric. Experimentation with SAR, Medical and Conventional images proved that the value of the metric gets increased with the increase in the compression ratio and gets reduces with the proposed adaptive algorithm. Results bestow the assessment slot in for the adaptiveness suggested is strong worthy in improving the visual quality both quantitatively and qualititatively.

A novel color image compression algorithm using the human visual contrast sensitivity characteristics

In order to achieve higher image compression ratio and improve visual perception of the decompressed image, a novel color image compression scheme based on the contrast sensitivity characteristics of the human visual system (HVS) is proposed. In the proposed scheme, firstly the image is converted into the YCrCb color space and divided into sub-blocks. Afterwards, the discrete cosine transform is carried out for each sub-block, and three quantization matrices are built to quantize the frequency spectrum coefficients of the images by combining the contrast sensitivity characteristics of HVS. The Huffman algorithm is used to encode the quantized data. The inverse process involves decompression and matching to reconstruct the decompressed color image. And simulations are carried out for two color images. The results show that the average structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR) under the approximate compression ratio could be increased by 2.78% and 5.48%, respectively, compared with the joint photographic experts group (JPEG) compression. The results indicate that the proposed compression algorithm in the text is feasible and effective to achieve higher compression ratio under ensuring the encoding and image quality, which can fully meet the needs of storage and transmission of color images in daily life.

Image Compression using DCT upon Various Quantization

Discrete cosine transform (DCT) is a widely compression technique for converting an image into elementary frequency components. However, level of quality and compression is desired, scalar multiples of the JPEG standard quantization may be used. In this paper, DCT method was applied to compress image under various level of quality. Different quantization matrices of DCT’s coefficients are used to improve level of quality and compression ratio of JPEG image.

Steganalysis of stegostorage system

The aim of this contribution is to detect the presence of messages in JPEG images that were stored in the files using sequential embedding into the least significant bits of DCT coefficients. When embedding, the StegoStorage system was used. For steganalysis, we used and implemented a process of calibration JPEG images, which makes it possible to investigate a histogram from steganographic images which are similar to the histogram of the original images without secret information. Also, the Pearson's chi-square test of goodness of fit was implemented, whose results indicate detectability of the presence of hidden messages through images in the two databases, the first database containing 1000 JPEG images and the second containing 450 JPEG images. An interesting result is that after saving messages into the images and compressing the JPEG once more, the detectability decreased in this case by almost half. Moreover, this contribution provides a framework for further testing by calculating the minimum square error (the weighted stego analysis), which also indicates detectability in both databases, and after embedding the messages into images and re-compressing them with different quantization matrix, the detectability is only slightly reduced. Therefore, we consider this method an appropriate one for further testing and utilizing it when embedding into the LSB randomly, without filling the full capacity of the carrier file.

Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA.

To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400–720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems.

Design of the Quantization Matrix in the Distributed Compressed Sensing Video Coding*

In the frame of compressed sensing distributed video coding, the design of the quantization matrix directly affects the reconstruction quality of the receiving terminal of the video. In this article, we present a new design method of the Gaussian quantization matrix adapting to the compressed sensing coding, for that the distribution of the parameters of the image is featured of the characteristic of approximately normal distribution after measured by compressive sensing. By this way, the parameters of a certain quantity of the image frames depending on the video sequences generated by the Gaussian quantization matrix possess certain adaptive capacity. By comparison with the plan of the traditional quantization, the quantization matrix presented in this article would improve the reconstruction quality of the video.

Denoising using Non Local Linear Filtering and Quantization Matrix Estimation Using ANFIS Algorithm for JPEG Error Analysis to Digital Image Forensics

One of the most commonly used image format is Joint Photographic Experts Group (JPEG). The recognition of JPEG compression plays a significant part in digital forensics. In previous work, JPEG image can be compressed upto n times. However, in the compression techniques noise of the JPEG images and the error analysis in the JPEG images are not primarily concentrated. Hence, the recognition of the JPEG compression results will turn out to be complicated. With the intention of overcoming these concern and eliminate the noise from the image samples, in this study formulated a blend of non local-means filter and its method noise thresholding by means of wavelets. In order to diminish the size of the JPEG image, a Growcut based seam carving technique is employed in this study. Subsequently noises are added to image to carry out Non local Linear Filter (NLF) and its Method Noise Thresholding by means of wavelets (NLFMT) denoising framework. For the purpose of assessing the influence of image compression on the performance of JPEG, a sample Discrete Cosine Transform-Singular Value Decomposition (DCT-SVD) was computed for single and double image compression, images were quantized by means of numerous quantization matrices, quantization matrix results are assessed with the help of Adaptive Neuro Fuzzy Inference System (ANFIS). Based on ANFIS, the elevated frequency coefficients in quantization matrix are employed to make a distinction among singly and doubly compressed images. Extensive experiments and evaluations with previous techniques reveal that the proposed DCT-SVD-ANFIS scheme can discover the double JPEG compression efficiently and noise in the image samples are eliminated with the help of NLFMT methods; it outperforms the existing approaches considerably based on the parameters like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The quantization matrix results were assessed using ANFIS; it has extremely much significance in the field of digital forensics.

Watermarking‐based image authentication robust to JPEG compression

A watermarking-based image authentication scheme in the discrete cosine transform (DCT) domain robust to JPEG compression is presented. The binary authentication code is generated from a pseudo-random sequence based on a secret key and a block-dependent feature, protecting the scheme against cut-and-paste attacks. The watermark is embedded in low-frequency DCT coefficients selected by the secret key using a modified quantisation index modulation approach. Before embedding, the selected coefficients are quantised using the JPEG quantisation matrix for a selected quality factor, protecting the scheme against JPEG compression with higher quality factors. Experimental results show that the proposed technique achieves very good watermark imperceptibility and is able to detect and locate malicious attacks with good precision. Compared with other existing schemes, the proposed algorithm achieves better performance regarding false positive and false negative detection rates and in discriminating malicious attacks from JPEG compression.

Detection of Duplicate Region and Hybrid Non-Local Means Filtering for Denoising with Quantization Matrix Estimation for JPEG Error Analysis

Background: The existing JPEG error analysis schemes do not offer agreeable results particularly when the duplicated area is small. Region duplication is an uncomplicated and efficient process to produce digital image forgeries, where a constant segment of pixels in an image, following feasible geometrical and illumination transformations are copied and pasted to a different location in the same image. Methods: In this research work, JPEG error analysis scheme is introduced for the purpose of consistent recognition of duplicated and distorted areas in a JPEG digital image forensics. Here, presented a new Multi-directional Curvelet Transform with Fourier Transform matching Invariant Rotation (MCFTIR) region duplication detection scheme to identify duplicated regions for JPEG images. This scheme begins with estimating the overlapping blocks of a JPEG image and it is organized in accordance with the statistics of multiple curvelet sub-bands. During the second phase, the amount of candidate block pairs of JPEG images has been significantly diminished by means of spatial distance between each pair of blocks for JPEG image. For duplicate region removed images theoretically analyzing the effects of these errors on single and double JPEG compression, with five major phases like Shape-Preserving Image Resizing (SPIR) scheme for the purpose of image resizing, noises are appended to image and eliminated with the help of Hybrid Non-Local Means Filtering (HNLMF) denoising framework, Image compression through Discrete Cosine Transform – Singular Value Decomposition (DCT-SVD) was computed for single and double image compression, images were quantized by means of numerous quantization matrices, quantization matrix results are estimated with Mamdani model based Adaptive Neural Fuzzy Inference System (MANFIS) and detecting the quantization table of a JPEG image. Findings: The proposed MCFTIR methods significantly outperform existing techniques in terms of the parameters like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) especially for the images of small sizes. It also show that the new MCFTIR method can consistently identify JPEG image blocks which are as tiny as 8x8 pixels and compressed through quality factors as elevated as 98. This performance is significant for the purpose of analyzing and locating small tampered regions inside a composite image.

Steganalysis of StegoStorage system

Abstract The aim of this contribution is to detect the presence of messages in JPEG images that were stored in the files using sequential embedding into the least significant bits of DCT coefficients. The Stegostorage system was used for embedding. For testing purposes, we set this system to the full capacity of cover files as the worst case from the view of security. For steganalysis, we used and implemented a process of calibration of JPEG images, which makes it possible to investigate a histogram from steganographic images which are similar to the histogram of the original images without secret information. Also, Pearson’s Chi- -square test of goodness of fit was implemented, whose results indicate detectability of the presence of hidden messages through images in the two databases, the first database containing 1000 JPEG images and the second one containing 450 JPEG images. An interesting result is that after saving messages into the images and compressing the JPEG once more, the detectability decreased in this case by almost half. Moreover, this contribution provides a framework for further testing by calculating the minimum square error (the weighted stego analysis), which also indicates detectability in both databases, and after embedding the messages into images and re-compressing them with different quantization matrix, the detectability is only slightly reduced. Therefore, we consider this method an appropriate one for further testing and utilizing it when embedding into the LSB randomly, without filling the full capacity of the carrier file.

On Image Forgery Detection Using Two Dimensional Discrete Cosine Transform and Statistical Moments

Abstract In the present communication, we propose a new approach for detecting copy-move forgery in digital images using statistical moments and two dimensional discrete cosine transform. We first slide a window centered around every pixel of the suspicious image, then each window is passed through two dimensional discrete cosine transform (2 D – DCT) to obtain the quantized coefficient matrix. The low dimensional statistical feature vector of each quantized coefficient matrix is obtained and arranged in a feature matrix F . The columns of F contain 4 - statistical features, i.e., mean Me , variance Var , third order moment skewness S k and fourth order moment kurtosis Kr obtained from the quantized coefficient matrix. In order to make similar windows adjacent, the feature matrix F is lexicographically sorted using radix sort. Finally, a copy-move forgery detection is performed using the adjacent pairs of feature vectors. It has also been observed that the proposed method has the lower dimension feature vector with lower computational complexity.