Modifications of uniform quantization applied in wavelet coder

The aim of this paper is to design, analyze and compare four different systems for ADC (analog-to-digital conversion) of vibration signals. Measurement of vibration signals is of particular importance in many areas, such as predictive maintenance or structural health monitoring. Wireless systems for vibration measurements becomes very topical, due to much easier and cheaper installation compared to wired systems. Due to the lack of transmission bandwidth and energy in wireless measurement systems, the amount of digital data being sent has to be reduced; hence, we have to apply ADC systems that can achieve the required digital signal quality, reducing the bit-rate. Four ADC systems are analyzed, for possible application in wireless measurement systems: PCM (pulse code modulation) based on uniform quantization; DPCM (differential PCM) to exploit high correlation of vibration signals; two adaptive ADC systems to cope with significant variations of characteristics of vibration signals in time - APCM (adaptive PCM) with adaptation on variance and ADPCM (adaptive DPCM), with double adaptation (both on variance and correlation). These ADC models are designed and optimized specifically for vibration signals, based on the analysis of 20 vibration signals from a referent database. An experiment is done, applying designed ADC systems for digitalization of vibration signals. APCM, DPCM and ADPCM systems allow significant bit-rate reduction compared to the PCM system, but with the increasing of complexity, hence the compromise between the bit-rate reduction and complexity is needed.

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Adaptive multimedia data hiding and watermarking

10.32920/ryerson.14655678 ◽

2021 ◽

Author(s):

Kan Li

Keyword(s):

Data Hiding ◽

Spread Spectrum ◽

Optimization Algorithms ◽

Wiener Filter ◽

Lagrangian Function ◽

Multimedia Data ◽

Digital Information ◽

Look Up Table ◽

Uniform Quantization ◽

Adaptive Multimedia

Watermarking is a technique of hiding a message about a work of media within that work itself in· the purpose of protecting the digital information against illegal duplication and manipulation. The objectives of this study are to analyze the robustness and distortion performance of watermarking system and to explore watermarking schemes which balance the robustness-distortion tradeoff optimally. In this thesis, We present a detector algorithm to adaptively extract spread spectrum watermark by filtering the watermarked images with Wiener filter. Two optimization algorithms for quantization watermarking are proposed. First one optimizes uniform quantization based look-up table embedding which minimizes watermarking distortion. Secondly, we analyze the robustness-distortion tradeoff and formulate the robustness-distortion tradeoff into a Lagrangian function. Hence optimal quantizers for watermarking subject to given robustness or fidelity constraint are achieved.

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Iterative Algorithm for Parameterization of Two-Region Piecewise Uniform Quantizer for the Laplacian Source

Mathematics ◽

10.3390/math9233091 ◽

2021 ◽

Vol 9 (23) ◽

pp. 3091

Author(s):

Jelena Nikolić ◽

Danijela Aleksić ◽

Zoran Perić ◽

Milan Dinčić

Keyword(s):

Neural Networks ◽

Performance Assessment ◽

Probability Density ◽

Probability Density Functions ◽

Density Functions ◽

Uniform Quantization ◽

Memory Footprint ◽

High Bit Rates ◽

And Performance ◽

Laplacian Distribution

Motivated by the fact that uniform quantization is not suitable for signals having non-uniform probability density functions (pdfs), as the Laplacian pdf is, in this paper we have divided the support region of the quantizer into two disjunctive regions and utilized the simplest uniform quantization with equal bit-rates within both regions. In particular, we assumed a narrow central granular region (CGR) covering the peak of the Laplacian pdf and a wider peripheral granular region (PGR) where the pdf is predominantly tailed. We performed optimization of the widths of CGR and PGR via distortion optimization per border–clipping threshold scaling ratio which resulted in an iterative formula enabling the parametrization of our piecewise uniform quantizer (PWUQ). For medium and high bit-rates, we demonstrated the convenience of our PWUQ over the uniform quantizer, paying special attention to the case where 99.99% of the signal amplitudes belong to the support region or clipping region. We believe that the resulting formulas for PWUQ design and performance assessment are greatly beneficial in neural networks where weights and activations are typically modelled by the Laplacian distribution, and where uniform quantization is commonly used to decrease memory footprint.

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Non-uniform Quantization in Breaking HUGO

Digital-Forensics and Watermarking - Lecture Notes in Computer Science ◽

10.1007/978-3-662-43886-2_4 ◽

2014 ◽

pp. 48-62 ◽

Cited By ~ 3

Author(s):

Licong Chen ◽

Yun Q. Shi ◽

Patchara Sutthiwan ◽

Xinxin Niu

Keyword(s):

Uniform Quantization

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Tampering Localization in Double Compressed Images by Investigating Noise Quantization

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2016070104 ◽

2016 ◽

Vol 8 (3) ◽

pp. 46-62

Author(s):

Archana Vasant Mire ◽

Sanjay B. Dhok ◽

Naresh J. Mistry ◽

Prakash D. Porey

Keyword(s):

Digital Images ◽

Forensic Analysis ◽

Experimental Results ◽

Quantization Noise ◽

Svm Classifier ◽

Compression Process ◽

Image Tampering ◽

Compressed Images ◽

Discrepancy Model ◽

Uniform Quantization

Noise is uniformly distributed throughout an untampered image. Tampering operations destroy this uniformity and introduce inconsistency in the tampered region. Hence, noise discrepancy is often investigated in forensic analysis of uncompressed digital images. However, noise in compressed images has got very little attention from the forensic experts. The JPEG compression process itself introduces uniform quantization noise throughout an image, making this investigation difficult. In this paper, the authors have proposed a new noise compression discrepancy model, which blindly estimates this discrepancy in the compressed images. Considering the smaller tampered region, SVM classifier was trained using noise features of test sub-images and its nonaligned recompressed versions. Each of the test sub-images was further classified using this classifier. Experimental results show that in some cases, the proposed approach can achieve better performance compared with other JPEG artefact based techniques.

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