Gate-Level Static Approximate Adders: A Comparative Analysis

Approximate or inaccurate addition is found to be viable for practical applications which have an inherent error tolerance. Approximate addition is realized using an approximate adder, and many approximate adder designs have been put forward in the literature targeting an acceptable trade-off between quality of results and savings in design metrics compared to the accurate adder. Approximate adders can be classified into three categories as: (a) suitable for FPGA implementation, (b) suitable for ASIC type implementation, and (c) suitable for FPGA and ASIC type implementations. Among these, approximate adders, which are suitable for FPGA and ASIC type implementations are particularly interesting given their versatility and they are typically designed at the gate level. Depending on the way approximation is built into an approximate adder, approximate adders can be classified into two kinds as static approximate adders and dynamic approximate adders. This paper compares and analyzes static approximate adders which are suitable for both FPGA and ASIC type implementations. We consider many static approximate adders and evaluate their performance for a digital image processing application using standard figures of merit such as peak signal to noise ratio and structural similarity index metric. We provide the error metrics of approximate adders, and the design metrics of accurate and approximate adders corresponding to FPGA and ASIC type implementations. For the FPGA implementation, we considered a Xilinx Artix-7 FPGA, and for an ASIC type implementation, we considered a 32/28 nm CMOS standard digital cell library. While the inferences from this work could serve as a useful reference to determine an optimum static approximate adder for a practical application, in particular, we found approximate adders HOAANED, HERLOA and M-HERLOA to be preferable.

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On the Implementation of Multi-bit Inexact Adder Cells and Application towards Image De-noising

Electronics ETF ◽

10.7251/els2024033b ◽

2020 ◽

Vol 24 (1) ◽

Author(s):

Srikant Kumar Beura ◽

Amol Arjun Jawale ◽

Bishnulatpam Pushpa Devi ◽

Prabir Saha

Keyword(s):

Signal To Noise Ratio ◽

Substantial Reduction ◽

Similarity Index ◽

Digital Signal ◽

Structural Similarity ◽

Propagation Delay ◽

Substantial Improvement ◽

Structural Similarity Index ◽

Error Metrics ◽

And Performance

Inexact computing is an attractive concept for digital signal processing at the submicron regime. This paper proposes 2-bit inexact adder cell and further escalate to 4-bit, and 8-bit inexact adder and error metrics have been evaluated mathematically for such adder cells. The approximated design has been proposed through the simplification of the K-Maps, which leads to a substantial reduction in the propagation delay as well as energy consumption. The proposed design has been verified through the Cadence Spectre and performance parameters (such as delay, power consumption) have been evaluated through CMOS gpdk45 nm technology. Furthermore, the proposed design has been applied to image de-noising application where the performance of the images like Peak Signal to Noise Ratio (PSNR), Normalized Correlation Coefficient (NCC) and Structural Similarity Index (SSIM) has been analyzed through MATLAB, which offer the substantial improvement from its counterpart.

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ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2020.v25i2.2691 ◽

2020 ◽

Vol 25 (2) ◽

pp. 86-97

Author(s):

Sandy Suryo Prayogo ◽

Tubagus Maulana Kusuma

Keyword(s):

Deep Learning ◽

Bit Error Rate ◽

Error Rate ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Signal To Noise ◽

Structural Similarity Index ◽

Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

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Joint Encryption and Compression-Based Watermarking Technique for Security of Digital Documents

ACM Transactions on Internet Technology ◽

10.1145/3414474 ◽

2021 ◽

Vol 21 (1) ◽

pp. 1-20

Author(s):

A. K. Singh ◽

S. Thakur ◽

Alireza Jolfaei ◽

Gautam Srivastava ◽

MD. Elhoseny ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Digital Data ◽

The Internet ◽

Discrete Wavelet ◽

Compression Performance ◽

Strong Compression ◽

Multiple Watermarks ◽

Value Decomposition

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.

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Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽

10.3390/s21165540 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5540

Author(s):

Nayeem Hasan ◽

Md Saiful Islam ◽

Wenyu Chen ◽

Muhammad Ashad Kabir ◽

Saad Al-Ahmadi

Keyword(s):

Signal To Noise Ratio ◽

Image Watermarking ◽

Similarity Index ◽

Structural Similarity ◽

Reconstructed Image ◽

Discrete Wavelet ◽

Signal To Noise ◽

Noise Ratio ◽

Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

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Approximate Full Adders for Energy Efficient Image Processing Applications

Journal of Circuits System and Computers ◽

10.1142/s0218126621502352 ◽

2021 ◽

pp. 2150235

Author(s):

M. C. Parameshwara

Keyword(s):

Energy Efficient ◽

State Of The Art ◽

Signal To Noise Ratio ◽

Full Adder ◽

Signal To Noise ◽

Design Metrics ◽

Standard Cell Library ◽

Cell Library ◽

Fair Comparison ◽

Power Delay Product

This paper proposes six novel approximate 1-bit full adders (AFAs) for inexact computing. The six novel AFAs namely AFA1, AFA2, AFA3, AFA4, AFA5, and AFA6 are derived from state-of-the-art exact 1-bit full adder (EFA) architectures. The performance of these AFAs is compared with reported AFAs (RAAs) in terms of design metrics (DMs) and peak-signal-to-noise-ratio (PSNR). The DMs under consideration are power, delay, power-delay-product (PDP), energy-delay-product (EDP), and area. For a fair comparison, the EFAs and proposed AFAs along with RAAs are described in Verilog, simulated, and synthesized using Cadences’ RC tool, using generic 180 nm standard cell library. The unconstrained synthesis results show that: among all the proposed AFAs, the AFA1 and AFA2 are found to be energy-efficient adders with high PSNR. The AFA1 has a total [Formula: see text][Formula: see text][Formula: see text]W, [Formula: see text][Formula: see text]ps, [Formula: see text][Formula: see text]fJ, [Formula: see text][Formula: see text]Js, [Formula: see text][Formula: see text][Formula: see text]m2, and [Formula: see text][Formula: see text]dB. And the AFA2 has the total [Formula: see text][Formula: see text][Formula: see text]W, [Formula: see text][Formula: see text]ps, [Formula: see text][Formula: see text]fJ, [Formula: see text][Formula: see text]Js, [Formula: see text][Formula: see text][Formula: see text]m2, and [Formula: see text][Formula: see text]dB.

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Cell-filtering-based multi-scale Shannon–Cosine wavelet denoising method for locust slice images

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319500358 ◽

2019 ◽

Vol 17 (05) ◽

pp. 1950035 ◽

Cited By ~ 3

Author(s):

Shenghan Mei ◽

Xiaochun Liu ◽

Shuli Mei

Keyword(s):

Signal To Noise Ratio ◽

Gaussian White Noise ◽

Similarity Index ◽

Structural Similarity ◽

Wavelet Denoising ◽

Experimental Results ◽

Transform Method ◽

Denoising Method ◽

Multi Scale ◽

Piecewise Smoothness

The locust slice images have all the features such as strong self-similarity, piecewise smoothness and nonlinear texture structure. Multi-scale interpolation operator is an effective tool to describe such structures, but it cannot overcome the influence of noise on images. Therefore, this research designed the Shannon–Cosine wavelet which possesses all the excellent properties such as interpolation, smoothness, compact support and normalization, then constructing multi-scale wavelet interpolative operator, the operator can be applied to decompose and reconstruct the images adaptively. Combining the operator with the local filter operator (mean and median), a multi-scale Shannon–Cosine wavelet denoising algorithm based on cell filtering is constructed in this research. The algorithm overcomes the disadvantages of multi-scale interpolation wavelet, which is only suitable for describing smooth signals, and realizes multi-scale noise reduction of locust slice images. The experimental results show that the proposed method can keep all kinds of texture structures in the slice image of locust. In the experiments, the locust slice images with mixture noise of Gaussian and salt–pepper are taken as examples to compare the performances of the proposed method and other typical denoising methods. The experimental results show that the Peak Signal-To-Noise Ratio (PSNR) of the denoised images obtained by the proposed method is greater 27.3%, 24.6%, 2.94%, 22.9% than Weiner filter, wavelet transform method, median and average filtering, respectively; and the Structural Similarity Index (SSIM) for measuring image quality is greater 31.1%, 31.3%, 15.5%, 10.2% than other four methods, respectively. As the variance of Gaussian white noise increases from 0.02 to 0.1, the values of PSNR and SSIM obtained by the proposed method only decrease by 11.94% and 13.33%, respectively, which are much less than other 4 methods. This shows that the proposed method possesses stronger adaptability.

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A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

Sensors ◽

10.3390/s19040946 ◽

2019 ◽

Vol 19 (4) ◽

pp. 946 ◽

Cited By ~ 2

Author(s):

Wenzhao Feng ◽

Chunhe Hu ◽

Yuan Wang ◽

Junguo Zhang ◽

Hao Yan

Keyword(s):

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Transmission Efficiency ◽

Signal Interference ◽

Progressive Transmission ◽

Wireless Multimedia ◽

Transmission Method ◽

Hierarchical Coding ◽

Object Region

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.

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Pothole Classification Model Using Edge Detection in Road Image

Applied Sciences ◽

10.3390/app10196662 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6662

Author(s):

Ji-Won Baek ◽

Kyungyong Chung

Keyword(s):

Edge Detection ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Image Data ◽

Structural Similarity ◽

Detection Algorithm ◽

Classification Model ◽

Specific Object ◽

Pixel Value ◽

Distortion Rate

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

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A compensating total variation image denoising model combining L1 and L2 norm

International Journal of Electrical Engineering Education ◽

10.1177/0020720920923305 ◽

2020 ◽

pp. 002072092092330

Author(s):

Liqiong Zhang ◽

Min Li ◽

Xiaohua Qiu

Keyword(s):

Image Denoising ◽

Total Variation ◽

Structural Information ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Isotropic Diffusion ◽

Model Combining ◽

L2 Norm ◽

Staircase Effect

To overcome the “staircase effect” while preserving the structural information such as image edges and textures quickly and effectively, we propose a compensating total variation image denoising model combining L1 and L2 norm. A new compensating regular term is designed, which can perform anisotropic and isotropic diffusion in image denoising, thus making up for insufficient diffusion in the total variation model. The algorithm first uses local standard deviation to distinguish neighborhood types. Then, the anisotropic diffusion based on L1 norm plays the role of edge protection in the strong edge region. The anisotropic and the isotropic diffusion simultaneously exist in the smooth region, so that the weak textures can be protected while overcoming the “staircase effect” effectively. The simulation experiments show that this method can effectively improve the peak signal-to-noise ratio and obtain the higher structural similarity index and the shorter running time.

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Color Image Generation from Range and Reflection Data of LiDAR

Sensors ◽

10.3390/s20185414 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5414

Author(s):

Hyun-Koo Kim ◽

Kook-Yeol Yoo ◽

Ho-Youl Jung

Keyword(s):

Color Image ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Point Clouds ◽

Range Data ◽

Fusion Method ◽

Image Generation ◽

Learning Networks ◽

Reflection Data

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways—early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.

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