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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Enhancing Intra Block Copy Prediction for Plenoptic 2.0 Video Coding under Macropixel Constraints

Enhancing Intra Block Copy Prediction for Plenoptic 2.0 Vide...

引用

2024 conference on visual communications and image processing

作者： Vinh Van Duong Thuc Nguyen Huu Yim, Jonghoon Jeon, Byeungwoo Sungkyunkwan Univ Dept Elect & Comp Engn Seoul South Korea Samsung Res Seoul South Korea

ISBN: (纸本)9798331529543;9798331529550

In this paper we introduce a novel approach to better utilize the intra block copy (IBC) prediction tool in encoding lenslet light field video (LFV) captured using plenoptic 2.0 cameras. Although the IBC tool has been recognized as promising for encoding LFV content, its fundamental limit due to its original design rooted for encoding conventional videos suggests slight modification possibility to better suit the property of LFV content. Observing the inherently large amount of repetitive image patterns due to the microlens array (MLA) structure of plenoptic cameras, several techniques are suggested in this paper to enhance the IBC coding tool itself for more efficiently encoding LFV contents. Our experimental results demonstrate that the proposed method significantly enhances the IBC coding performance in case of encoding LFV contents while concurrently reducing encoding time.

关键词： Light field plenoptic video coding fast motion estimation video coding microlens image

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End-to-end image Compression with Swin-Transformer

End-to-end Image Compression with Swin-Transformer

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Wang, Meng Zhang, Kai Zhang, Li Li, Yue Li, Junru Wang, Yue Wang, Shiqi City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Bytedance Inc San Diego CA 92122 USA Beijing Bytedance Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose an end-to-end image compression framework, which cooperates with the swin-transformer modules to capture the localized and non-localized similarities in image compression. In particular, the swin-transformer modules are deployed in the analysis and synthesis stages, interleaving with convolution layers. The transformer layers are expected to perceive more flexible receptive fields, such that the spatially localized and non-localized redundancies could be more effectively eliminated. The proposed method reveals the excellent capability of signal conjunction and prediction, leading to the improvement of the rate and distortion performance. Experimental results show that the proposed method is superior to the existing methods on both natural scene and screen content images, where 22.46% BD-Rate savings are achieved when compared with the BPG. Over 30% BD-Rate gains could be observed with screen content images when compared with the classical hyper-prior end-to-end coding method.

关键词： image compression end-to-end compression transformer convolution

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HYPERSPECTRAL image visualIZATION BASED ON HIGH DYNAMIC RANGE IMAGING

HYPERSPECTRAL IMAGE VISUALIZATION BASED ON HIGH DYNAMIC RANG...

引用

22nd IEEE Signal processing and communications Applications conference (SIU)

作者： Suer, Secil Koc, Hatice Erturk, Sarp Kocaeli Univ Isaret & Goruntu Isleme Lab KULIS Elekt & Haberlesme Muh Bolumu TR-41380 Kocaeli Turkey

ISBN: (纸本)9781479948741

Hyperspectral imaging captures a high number of spectrally narrow bands and provides advantages for image analysis applications such as identification and classification in particular. However, for the visual inspection of hyperspectral images, the data is conventionally converted to a standard color image format. It is important that as much detail and data as possible is retained during this conversion. A novel hyperspectral visualization approach based on high dynamic range imaging is presented in this paper. The proposed approach retains visual detail and provides a superior result in terms of visual quality.

关键词： Spectroscopy

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Learn to overfit better: finding the important parameters for learned image compression

Learn to overfit better: finding the important parameters fo...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Zhang, Honglei Cricri, Francesco Tavakoli, Hamed Rezazadegan Santamaria, Maria Lam, Yat-Hong Hannuksela, Miska M. Nokia Technol Espoo Finland

ISBN: (纸本)9781728185514

For most machine learning systems, overfitting is an undesired behavior. However, overfitting a model to a test image or a video at inference time is a favorable and effective technique to improve the coding efficiency of learning-based image and video codecs. At the encoding stage, one or more neural networks that are part of the codec are finetuned using the input image or video to achieve a better coding performance. The encoder encodes the input content into a content bitstream. If the finetuned neural network is part (also) of the decoder, the encoder signals the weight update of the finetuned model to the decoder along with the content bitstream. At the decoding stage, the decoder first updates its neural network model according to the received weight update, and then proceeds with decoding the content bitstream. Since a neural network contains a large number of parameters, compressing the weight update is critical to reducing bitrate overhead. In this paper, we propose learning-based methods to find the important parameters to be overfitted, in terms of rate-distortion performance. Based on simple distribution models for variables in the weight update, we derive two objective functions. By optimizing the proposed objective functions, the importance scores of the parameters can be calculated and the important parameters can be determined. Our experiments on lossless image compression codec show that the proposed method significantly outperforms a prior-art method where overfitted parameters were selected based on heuristics. Furthermore, our technique improved the compression performance of the state-of-the-art lossless image compression codec by 0.1 bit per pixel.

关键词： image compression

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Frequency-Aware Hierarchical image Compression for Humans and Machines

Frequency-Aware Hierarchical Image Compression for Humans an...

引用

2024 conference on visual communications and image processing

作者： Luo, Yue Zhang, Zixiang Kuang, Jinhao Yu, Li Huazhong Univ Sci & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

To achieve efficient compression for both human vision and machine perception, scalable coding methods have been proposed in recent years. However, existing methods do not fully eliminate the redundancy between features corresponding to different tasks, resulting in suboptimal coding performance. In this paper, we propose a frequency-aware hierarchical image compression framework designed for humans and machines. Specifically, we investigate task relationships from a frequency perspective, utilizing only HF information for machine vision tasks and leveraging both HF and LF features for image reconstruction. Besides, the residual block embedded octave convolution module is designed to enhance the information interaction between HF features and LF features. Additionally, a dual-frequency channel-wise entropy model is applied to reasonably exploit the correlation between different tasks, thereby improving multi-task performance. The experiments show that the proposed method offers -69.3%similar to-75.3% coding gains on machine vision tasks compared to the relevant benchmarks, and -19.1% gains over state-of-the-art scalable image codec in terms of image reconstruction quality.

关键词： scalable image coding image coding for machines learned image compression

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EXTENDING CCSDS 123.0-B-1 FOR LOSSLESS 4D image COMPRESSION

EXTENDING CCSDS 123.0-B-1 FOR LOSSLESS 4D IMAGE COMPRESSION

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Panpan Wang, Xiuheng Gao, Tiande Feng, Zhenfu Chen, Jie Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Sch Marine Sci & Technol Xian Shaanxi Peoples R China Xian Univ Posts & Telecommun Sch Elect Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781728180687

A 4-dimensional (4D) image can be viewed as a stack of volumetric images over channels of observation depth or temporal frames. This data contains rich information at the cost of high demands for storage and transmission resources due to its large volume. In this paper, we present a lossless 4D image compression algorithm by extending CCSDS-123.0-B-1 standard. Instead of separately compressing the volumetric image at each channel of 4D images, the proposed algorithm efficiently exploits redundancy across the fourth dimension of data. Experiments conducted on two types of 4D images demonstrate the effectiveness of the proposed lossless compression method.

关键词： 4D images lossless compression CCSDS-123.0-B-1 channel redundancy

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News image Steganography: A Novel Architecture Facilitates the Fake News Identification

News Image Steganography: A Novel Architecture Facilitates t...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhou, Jizhe Pun, Chi-Man Tong, Yu Univ Macao Dept Comp & Informat Sci Macau Peoples R China Vivo Mobile Commun Co Ltd Vivo AI Lab Shenzhen Peoples R China

ISBN: (纸本)9781728180687

A larger portion of fake news quotes untampered images from other sources with ulterior motives rather than conducting image forgery. Such elaborate engraftments keep the inconsistency between images and text reports stealthy, thereby, palm off the spurious for the genuine. This paper proposes an architecture named News image Steganography (NIS) to reveal the aforementioned inconsistency through image steganography based on GAN. Extractive summarization about a news image is generated based on its source texts, and a learned steganographic algorithm encodes and decodes the summarization of the image in a manner that approaches perceptual invisibility. Once an encoded image is quoted, its source summarization can be decoded and further presented as the ground truth to verify the quoting news. The pairwise encoder and decoder endow images of the capability to carry along their imperceptible summarization. Our NIS reveals the underlying inconsistency, thereby, according to our experiments and investigations, contributes to the identification accuracy of fake news that engrafts untampered images.

关键词： image steganography information hiding fake news identification

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Detection and classification of painting defects using deep learning 36

Detection and classification of painting defects using deep ...

引用

36th International Technical conference on Circuits/Systems, Computers and communications (ITC-CSCC)

作者： Adachi, Kazune Natori, Takahiro Aikawa, Naoyuki Tokyo Univ Sci Tokyo Japan

ISBN: (纸本)9781665435536

In the visual inspection, the quality assurance is difficult, because the dispersion occurs in the result by skill and fatigue degree of the inspector. Recently, a visual inspection method by image processing using deep learning has been proposed. When using deep learning, the dataset to be used is important. In this paper, we describe a method for detecting painting defects using image processing, automatically generating data for deep learning, and using these data for classification using deep learning.

关键词： Painting visual inspection Deep learning image processing

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3D Tensor Display for Non-Lambertian Content

3D Tensor Display for Non-Lambertian Content

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Losfeld, Armand Soetens, Eline Bonatto, Daniele Fachada, Sarah Van Bogaert, Laurie Lafruit, Gauthier Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium

ISBN: (纸本)9781665475921

A tensor display is a type of 3D light field display, composed of multiple transparent screens and a back-light that can render a scene with correct depth, allowing to view a 3D scene without wearing glasses. The analysis of state-of-the-art tensor displays assumes that the content is Lambertian. In order to extend its capabilities, we analyze the limitations of displaying non-Lambertian scenes and propose a new method to factorize the non-Lambertian scenes using disparity analysis. Moreover, we demonstrate a new prototype of a tensor display with three layers of full HD content at 60 fps. Compared with state-of-the-art, the evaluation results verify that the proposed non-Lambertian rendering method can display a higher quality for non-Lambertian scenes on both simulation and a prototyped tensor display.

关键词： Non-Lambertian prototype tensor display depth range disparity range simulation light field

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iWave3D: End-to-end Brain image Compression with Trainable 3-D Wavelet Transform

iWave3D: End-to-end Brain Image Compression with Trainable 3...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Xue, Dongmei Ma, Haichuan Li, Li Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728185514

With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods. At present, the most commonly used compression methods are all based on 3-D wavelet transform, such as JP3D. However, traditional 3-D wavelet transforms are designed manually with certain assumptions on the signal, but brain images are not as ideal as assumed. What's more, they are not directly optimized for compression task. In order to solve these problems, we propose a trainable 3-D wavelet transform based on the lifting scheme, in which the predict and update steps are replaced by 3-D convolutional neural networks. Then the proposed transform is embedded into an end-to-end compression scheme called iWave3D, which is trained with a large amount of brain images to directly minimize the rate-distortion loss. Experimental results demonstrate that our method outperforms JP3D significantly by 2.012 dB in terms of average BD-PSNR.

关键词： Brain image compression wavelet transform lifting scheme 3-D convolutional neural networks

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