Quantum Image Scaling Based on Bilinear Interpolation with Decimals Scaling Ratio

2021 ◽  
Author(s):  
Ri-Gui Zhou ◽  
Chuan Wan
Keyword(s):  
Bilinear Interpolation ◽  
Quantum Image ◽  
Image Scaling

Bilinear Interpolation Image Scaling Processor for VLSI Architecure

2014 ◽  
Vol 3 (3) ◽  
pp. 104 ◽  
Author(s):  
Pawar Ashwini Dilip ◽  
K Rameshbabu ◽  
Kanase Prajakta Ashok ◽  
Shital Arjun Shivdas
Keyword(s):  
Vlsi Architecture ◽  
Spatial Filter ◽  
Cmos Process ◽  
Interpolation Algorithm ◽  
Bilinear Interpolation ◽  
Hardware Cost ◽  
Image Scaling ◽  
Memory Buffer ◽  
Convolution Kernels ◽  
Calculation Unit

We introduce image scaling processor using VLSI technique. It consist of Bilinear interpolation, clamp filter and  a sharpening spatial filter. Bilinear interpolation algorithm is popular due to its computational efficiency and  image quality. But resultant image consist of blurring edges and aliasing artifacts after scaling. To reduce the blurring and aliasing artifacts sharpening spatial filter and clamp filters are used as pre-filter. These filters are realized by using T-model and inversed T-model convolution kernels. To reduce the memory buffer and computing resources for proposed image processor design two T-model or inversed T-model filters are combined into combined filter which requires only one line buffer memory. Also, to reduce hardware cost Reconfigurable calculation unit (RCU)is invented. The VLSI architecture in this work can achieve 280 MHz with 6.08-K gate counts, and its core area is 30 378 <em>μ</em>m2 synthesized by a 0.13-<em>μ</em>m CMOS process.

Scaling of Engineering Image Based on Interpolation Algorithm

2013 ◽  
Vol 634-638 ◽  
pp. 3989-3993
Author(s):  
Hui Wang ◽  
Guo Jia Li ◽  
Jun Hui Pan
Keyword(s):  
Image Quality ◽  
Experimental Results ◽  
Interpolation Algorithm ◽  
Bilinear Interpolation ◽  
Large Capacity ◽  
Image Scaling

Before the large capacity and engineering image is analyzed carefully, which need to be effective scaled. The subsequent analysis and calculation to engineering image is subjected by image quality and scaling time. According to scaling research of large capacity engineering image, the effect for image scaling by various interpolation algorithm is individual analyzed, and more appropriate algorithm is selected. The experimental results show that the engineering image of best effect is got, when it is high-expansion scaled by double cubic interpolation, and the bilinear interpolation is more suitable for low multiple scaling image.

Bilinear interpolation method for quantum images based on quantum Fourier transform

2018 ◽  
Vol 16 (04) ◽  
pp. 1850031 ◽  
Author(s):  
Panchi Li ◽  
Xiande Liu
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Nearest Neighbor ◽  
Interpolation Method ◽  
New Method ◽  
Basic Operation ◽  
Quantum Fourier Transform ◽  
Bilinear Interpolation ◽  
Image Scaling ◽  
Bicubic Interpolation

Image scaling is the basic operation that is widely used in classic image processing, including nearest-neighbor interpolation, bilinear interpolation, and bicubic interpolation. In quantum image processing (QIP), the research on image scaling is focused on nearest-neighbor interpolation, while the related research of bilinear interpolation is very rare, and that of bicubic interpolation has not been reported yet. In this study, a new method based on quantum Fourier transform (QFT) is designed for bilinear interpolation of images. Firstly, some basic functional modules are constructed, in which the new method based on QFT is adopted for the design of two core modules (i.e. addition and multiplication), and then these modules are used to design quantum circuits for the bilinear interpolation of images, including scaling-up and down. Finally, the complexity analysis of the scaling circuits based on the elementary gates is deduced. Simulation results show that the scaling image using bilinear interpolation is clearer than that using the nearest-neighbor interpolation.

scholarly journals Scaling Up and Down of 3-D Floating-point Data in Quantum Computation

2021 ◽  
Author(s):  
Meiyu Xu ◽  
Dayong Lu ◽  
Xiaoyun Sun
Keyword(s):  
Quantum Computation ◽  
Scaling Up ◽  
Floating Point ◽  
Geometric Transformation ◽  
Double Precision ◽  
Quantum Image ◽  
Image Scaling ◽  
Trilinear Interpolation ◽  
Point Data ◽  
Floating Point Numbers

Abstract In the past few decades, quantum computation has become increasingly attractivedue to its remarkable performance. Quantum image scaling is considered a common geometric transformation in quantum image processing, however, the quantum floating-point data version of which does not exist. Is there a corresponding scaling for 2-D and 3-D floating-point data? The answer is yes.In this paper, we present quantum scaling up and down scheme for floating-point data by using trilinear interpolation method in 3-D space. This scheme offers better performance (in terms of the precision of floating-point numbers) for realizing the quantum floating-point algorithms compared to previously classical approaches. The Converter module we proposed can solve the conversion of fixed-point numbers to floating-point numbers of arbitrary size data with p + q qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double precision or 128-bit extended-precision. Usually, we use nearest neighbor interpolation and bilinear interpolation to achieve quantum image scaling algorithms, which are not applicable in high-dimensional space. This paper proposes trilinear interpolation of floating-point numbers in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the circuits of quantum scaling up and down for 3-D floating-point data are designed.

Quantum multidimensional color image scaling using nearest-neighbor interpolation based on the extension of FRQI

2017 ◽  
Vol 31 (17) ◽  
pp. 1750184 ◽  
Author(s):  
Ri-Gui Zhou ◽  
Canyun Tan ◽  
Ping Fan
Keyword(s):  
Nearest Neighbor ◽  
Complexity Analysis ◽  
Color Image ◽  
Scaling Up ◽  
Quantum Image ◽  
Image Scaling ◽  
Lower Complexity ◽  
Scaling Down ◽  
Classical Computer ◽  
First Time

Reviewing past researches on quantum image scaling, only 2D images are studied. And, in a quantum system, the processing speed increases exponentially since parallel computation can be realized with superposition state when compared with classical computer. Consequently, this paper proposes quantum multidimensional color image scaling based on nearest-neighbor interpolation for the first time. Firstly, flexible representation of quantum images (FRQI) is extended to multidimensional color model. Meantime, the nearest-neighbor interpolation is extended to multidimensional color image and cycle translation operation is designed to perform scaling up operation. Then, the circuits are designed for quantum multidimensional color image scaling, including scaling up and scaling down, based on the extension of FRQI. In addition, complexity analysis shows that the circuits in the paper have lower complexity. Examples and simulation experiments are given to elaborate the procedure of quantum multidimensional scaling.

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