Low Complexity Orthogonal Transforms for Low Cost Image/Video Codec Design

Ezhilarasi, R.; Venkatalakshmi, K.

doi:10.1166/jctn.2018.7167

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the 3D depth convolution stage, the convolution kernel is only calculated separately with each channel of each frame of the input sequence, and they are not combined. It is a new feature; in the three-dimensional point-by-point convolution stage, a convolution kernel of size 1 × 1 × 1 is used for convolution [29]. us, the calculation process has the following formula:…”

Section: Optimizationmentioning

confidence: 99%

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Wang

Hawbani

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.

show abstract

Section: Optimizationmentioning

confidence: 99%

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Wang

Hawbani

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In the context of the experience economy era, when people receive external information, they need the participation of various sensory systems. Today's society pays more and more attention to the humanization and individualization of design and emphasizes the basis of people's experience; so, art design should pay attention to other sensory functions [19,20]. The content of sensory experience mainly includes the following three aspects: 2.3.1.…”

Section: Sensory Experiencementioning

confidence: 99%

Image Sensory Experience of Artistic Design Based on the Role of Omnidirectional Vision Sensors

Tong¹

2021

Journal of Sensors

View full text Add to dashboard Cite

As a sensor with a wide field of view, the panoramic vision sensor is efficient and convenient in perceiving the characteristic information of the surrounding environment and plays an important role in the experience of artistic design of images. The transformation of visual and other sensory experiences in art design is to integrate sound, image, texture, taste, and smell with each other through reasonable rules, to create more excellent crossborder art design works. To improve the sensory experience that art design works bring to the audience, the combination of vision and other sensory experiences can maximize the advantages of multiple information dissemination methods and combine the omnidirectional visual sensor with the sensory experience of art design images. In the method part, this article introduces the omnidirectional vision sensor, art design image, and sensory experience modes and content and introduces the hyperbolic concave mirror theory and the Micusik perspective projection imaging model. In the experimental part, the experimental environment, experimental objects, and experimental procedures of this article are introduced. In the analysis part, this article analyzes the six aspects of image database dependency test, performance, comparison of different distortion types, false detection rate and missing detection rate, algorithm time-consuming comparison, sensory experience analysis, and feature point screening. Among the feelings of the art design image, for the first image, 87.21% of the audience’s feelings are happy, indicating that the main idea of this image can bring joy to people. In the second image, the audience’s feelings are mostly sad. For the third image, more than half of the audience’s feelings are melancholy. For the fourth image, 69.34% of the audience’s inner feelings are calm. It explains that the difference in the content of art design images can bring different sensory experiences to people.

show abstract

“…e orthogonal transform is a good mathematical tool and has been used in many applications of digital signal processing, such as harmonic analysis, numerical computation, image processing, data compression, and information hiding [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Generalized Orthogonal Discrete W Transform and Its Fast Algorithm

Sun

Zhang

2021

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Based on the generalized discrete Fourier transform, the generalized orthogonal discrete W transform and its fast algorithm are proposed and derived in this paper. The orthogonal discrete W transform proposed by Zhongde Wang has only four types. However, the generalized orthogonal discrete W transform proposed by us has infinite types and subsumes a family of symmetric transforms. The generalized orthogonal discrete W transform is a real-valued orthogonal transform, and the real-valued orthogonal transform of a real sequence has the advantages of simple operation and facilitated transmission and storage. The generalized orthogonal discrete W transforms provide more basis functions with new frequencies and phases and hence lead to more powerful analysis and processing tools for communication, signal processing, and numerical computing.

show abstract

Low Complexity Orthogonal Transforms for Low Cost Image/Video Codec Design

Cited by 3 publications

References 0 publications

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Image Sensory Experience of Artistic Design Based on the Role of Omnidirectional Vision Sensors

Generalized Orthogonal Discrete W Transform and Its Fast Algorithm

Contact Info

Product

Resources

About