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Relevant bibliographies by topics / Quantization error

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Journal articles

Academic literature on the topic 'Quantization error'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Quantization error"

1

Dai, Xinyan, Xiao Yan, Kelvin K. W. Ng, Jiu Liu, and James Cheng. "Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 51–58. http://dx.doi.org/10.1609/aaai.v34i01.5333.

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Vector quantization (VQ) techniques are widely used in similarity search for data compression, computation acceleration and etc. Originally designed for Euclidean distance, existing VQ techniques (e.g., PQ, AQ) explicitly or implicitly minimize the quantization error. In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error. We show that quantization errors in norm have much higher influence on inner products than quantization errors in direction, and small quantization error does not necessarily lead to good performance in maximum inner product search (MIPS). Based on this observation, we propose norm-explicit quantization (NEQ) — a general paradigm that improves existing VQ techniques for MIPS. NEQ quantizes the norms of items in a dataset explicitly to reduce errors in norm, which is crucial for MIPS. For the direction vectors, NEQ can simply reuse an existing VQ technique to quantize them without modification. We conducted extensive experiments on a variety of datasets and parameter configurations. The experimental results show that NEQ improves the performance of various VQ techniques for MIPS, including PQ, OPQ, RQ and AQ.

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2

Qin, Li Juan. "Robustness Problem Argumentation from Image Quantization Errors in Vision Location." Advanced Materials Research 225-226 (April 2011): 1332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.1332.

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In our vision location system, error is inevitable. Image quantization errors play an important role in computer vision field. Quantization errors are the primary sources that affect the precision of pose estimation and they are inherent and unavoidable. It is important to analysis on the effect of this error on compute process. In this paper, Robustness problem argumentation in vision location is presented in detail. Then we introduce image quantization error. Robustness mathematical model for vision location is set up at last.

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3

Blinn, J. F. "Quantization error and dithering." IEEE Computer Graphics and Applications 14, no. 4 (1994): 78–82. http://dx.doi.org/10.1109/38.291534.

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4

Zheng, Dong Xi. "The Realization and Comparison of Directly Fitting Method and Edge Extraction Method Used in Amending the Quantization Errors." Advanced Materials Research 546-547 (July 2012): 537–41. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.537.

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The quantization error is one of errors in 3D measuring, the method of amending the quantization errors includes directly fitting method and edge extraction method. Analyzed the source of quantization errors and the principle to amend the quantization errors, and analyzed the simulation test of directly fitting method and edge extraction method, and compared them with each other.

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5

Chen, Congliang, Li Shen, Haozhi Huang, and Wei Liu. "Quantized Adam with Error Feedback." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (2021): 1–26. http://dx.doi.org/10.1145/3470890.

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In this article, we present a distributed variant of an adaptive stochastic gradient method for training deep neural networks in the parameter-server model. To reduce the communication cost among the workers and server, we incorporate two types of quantization schemes, i.e., gradient quantization and weight quantization, into the proposed distributed Adam. In addition, to reduce the bias introduced by quantization operations, we propose an error-feedback technique to compensate for the quantized gradient. Theoretically, in the stochastic nonconvex setting, we show that the distributed adaptive gradient method with gradient quantization and error feedback converges to the first-order stationary point, and that the distributed adaptive gradient method with weight quantization and error feedback converges to the point related to the quantized level under both the single-worker and multi-worker modes. Last, we apply the proposed distributed adaptive gradient methods to train deep neural networks. Experimental results demonstrate the efficacy of our methods.

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6

Ti-Chiun Chang and J. P. Allebach. "Quantization of accumulated diffused errors in error diffusion." IEEE Transactions on Image Processing 14, no. 12 (2005): 1960–76. http://dx.doi.org/10.1109/tip.2005.859372.

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7

Zhang, Qiu Ju, Yong Chang Wang, and Kai Liu. "The Theory of Using an Intensity-Correcting Algorithm to Overcome Quantization Error for Phase Measuring Profilometry." Advanced Materials Research 718-720 (July 2013): 1170–74. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1170.

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In the image process, intensity differs from its true value because the quantization process restricts image pixels to lie on an integer grid, and phase quantization error is introduced. In this paper, we propose a theory of using an intensity-correcting to overcome phase quantization error. According to the distribution of the intensity error in some pixels, the mathematical model of the intensity error is reconstructed to correct intensity values and reduce phase quantization error. Using specific example deduct the intensity-correction algorithm. At last, we compare the uncorrected quantization error and the quantization error after correction, and prove that the principle of this algorithm is right.

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8

Saini, Deepika, and Sanjeev Kumar. "Quantization error in stereo imaging system with noise distributions." International Journal of Modeling, Simulation, and Scientific Computing 11, no. 05 (2020): 2050042. http://dx.doi.org/10.1142/s1793962320500427.

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The problem of estimating quantization error in 2D images is an inherent problem in computer vision. The outcome of this problem is directly related to the error in reconstructed 3D position coordinates of an object. Thus estimation of quantization error has its own importance in stereo vision. Although the quantization error cannot be controlled fully, still statistical error analysis helps us to measure the performance of stereo systems that relies on the imaging parameters. Generally, it is assumed that the quantization error in 2D images is distributed uniformly that need not to be true from a practical aspect. In this paper, we have incorporated noise distributions (Triangular and Trapezoidal) for the stochastic error analysis of the quantization error in stereo imaging systems. For the validation of the theoretical analysis, the detailed simulation study is carried out by considering different cases.

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9

NAGASHIMA, Tomotaka, Makoto HASEGAWA, Takuya MURAKAWA, and Tsuyoshi KONISHI. "Quantization Error Improvement for Optical Quantization Using Dual Rail Configuration." IEICE Transactions on Electronics E98.C, no. 8 (2015): 808–15. http://dx.doi.org/10.1587/transele.e98.c.808.

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10

Cadiz, Rodrigo F., Camila Muñoz, Cristian Tejos, Marcelo E. Andia, Sergio Uribe, and Pablo Irarrazaval. "Quantization error in magnetic resonance imaging." Concepts in Magnetic Resonance Part A 43A, no. 3 (2014): 79–89. http://dx.doi.org/10.1002/cmr.a.21303.

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