High-Efficiency Image Coding via Near-Optimal Filtering

Zhang, Xinfeng; Wang, Shiqi; Zhang, Yabin; Wang, Lin; Ma, Siwei; Gao, Wen

doi:10.1109/lsp.2017.2732680

Cited by 13 publications

(2 citation statements)

References 22 publications

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“…In tasks like image recognition neural networks have achieved great success; handwriting recognition and optical character [43] [44]. In fact, the recent explosive advancement of deep learning [45] has resulted in the neural networks that are composed of several different layers of learners. In a task of image recognition, for example, where the machine has to classify what types of objects are in an image one layer might learn where the lines are in an image, while another layer could learn how these lines organize to represent different forms (e.g., books vs. people vs. pets).…”

Section: Neural Networkmentioning

confidence: 99%

Image Compression Using Neural Networks: A Review

et al. 2021

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Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

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Section: Neural Networkmentioning

confidence: 99%

Image Compression Using Neural Networks: A Review

et al. 2021

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“…Lossy image compression techniques such as JPEG and JPEG2000 achieved high compression ratios at the cost of perceived degradation in image quality [1,2,3]. The stateof-the-art image compression systems usually need a quality metric to optimize the image coding procedure by assigning bits to various image contents adaptively.…”

Section: Introductionmentioning

confidence: 99%

Compressed Image Quality Assessment Based on Saak Features

Zhang

Kuo

2019

2019 IEEE International Conference on Image Processing (ICIP)

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Compressed image quality assessment plays an important role in image services, especially in image compression applications, which can be utilized as a guidance to optimize image processing algorithms. In this paper, we propose an objective image quality assessment algorithm to measure the quality of compressed images. The proposed method utilizes a datadriven transform, Saak (Subspace approximation with augmented kernels), to decompose images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the stateof-the-art image quality assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.

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A Dual-Network Based Super-Resolution for Compressed High Definition Video

Feng

Zhang

et al. 2018

Lecture Notes in Computer Science

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High-Efficiency Image Coding via Near-Optimal Filtering

Cited by 13 publications

References 22 publications

Image Compression Using Neural Networks: A Review

Image Compression Using Neural Networks: A Review

Compressed Image Quality Assessment Based on Saak Features

A Dual-Network Based Super-Resolution for Compressed High Definition Video

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