Abstract
In this paper, piezoelectric sensing elements are used to assist in the study and analysis of ceramic art process optimization and visual quantization characteristics. A series piezoelectric element impedance sensor is designed based on the resonant frequency characteristics of the series piezoelectric element. Combining the resonant frequency characteristics of the series piezoelectric element and the basic principle of the impedance method, a multisensing impedance method based on the series piezoelectric element impedance sensor is proposed. The feasibility of the multisensing impedance method for monitoring the grout compactness was verified experimentally, and the basic principle of the method was further investigated by finite element simulation. The vase-type porcelain vessels were classified according to symmetry elements to find the characteristic points, the abdominal morphology was used as the basis for classification, and the screened samples were extracted from the contours to exclude the influence of other factors on the vessel shape. By the symmetrical elements of each type of ware, the classification principle of the ware type was designed and divided into six types, and each type was further subdivided into various types to establish a typological map of Qing dynasty bottle porcelain. The information entropy redundancy that describes the uniformity of the code appearance probability and the visual redundancy that describes the human eye’s sensitivity to image content or details are all entry points that can be considered for image coding. The experimental results show that the LBP-HOG fusion features can digitally express the information of ancient ceramic ornamentation and dig and verify the evolution of ceramic ornamentation with the times from the digital quantity. The GRNN model has an excellent performance in processing small samples of ancient ceramic data.
Highlights
With the rapid development of computer and multimedia technology, the era of information and big data has come
Including spatial redundancy describing the correlation of pixels at different locations of the image, structural redundancy describing the presence of strong texture in a particular region of the image, information entropy redundancy describing the probability of code occurrence uniformity, and visual redundancy describing the human eye’s sensitivity to image content or details are all entry points that can be considered for image coding [2]
For the visual redundant information carried by the image, HEVC adopts a method based on structural similarity to objectively evaluate the image distortion, which considers the visual characteristics of the human eye and achieves the estimation of image perceptual distortion to a certain extent, but the method is mainly used to evaluate the distortion of the reconstructed image, while in the actual image coding process based on rate-distortion optimization theory, HEVC uses the absolute difference between the original and reconstructed pixels to represent the pixel distortion of the Journal of Sensors image, and the reconstructed image obtained by coding without considering the visual characteristics of the human eye is bound to carry a large amount of visual redundant information
Summary
With the rapid development of computer and multimedia technology, the era of information and big data has come. The core idea of image coding is to eliminate all kinds of redundant information in images. The elimination of all kinds of redundant information as much as possible, while ensuring that the reconstructed image meets certain imaging quality requirements, is what needs to be accomplished and the goal of image coding. Consider the application of the reconstructed image, which can be only oriented to feature extraction for data processing or image display for end devices
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