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检索条件"任意字段=2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023"

共 2296 条记录，以下是211-220 订阅

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Volumetric End-to-End Optimized Compression for Brain images

Volumetric End-to-End Optimized Compression for Brain Images

引用

ieee international conference on visual communications and image processing (vcip)

作者： Gao, Shuo Zhang, Yueyi Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728180687

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.

关键词： Volumetric image compression 3D convolutional neural network convolutional long short-term memory

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3D-CNN Autoencoder for Plenoptic image Compression

3D-CNN Autoencoder for Plenoptic Image Compression

引用

ieee international conference on visual communications and image processing (vcip)

作者： Zhong, Tingting Jin, Xin Tong, Kedeng Tsinghua Shenzhen Int Grad Sch Shenzhen Key Lab Broadband Network & Multimedia Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728180687

Recently, plenoptic image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and transmission. In order to adapt compression to the structural characteristic of plenoptic image, in this paper, we propose a Data Structure Adaptive 3D-convolutional(DSA-3D) autoencoder. The DSA-3D autoencoder enables up-sampling and down-samping the sub-aperture sequence along the angular resolution or spatial resolution, thereby avoiding the artifacts caused by directly compressing plenoptic image and achieving better compression efficiency. In addition, we propose a special and efficient S quare rearrangement to generate sub-aperture sequence. We compare Square with Zigzag sub-aperture sequence rearrangements, and analyzed the compression efficiency of block image compression and whole image compression. Compared with traditional hybrid encoders HEVC, JPEG2000 and JPEG PLENO(WaSP), the proposed DSA-3D(Square) autoencoder achieves a superior performance in terms of PSNR metrics.

关键词： Plenoptic image compression Autoencoder network 3D-CNN

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Anchoring Vision and Language Knowledge for Weakly Supervised Group Activity Recognition

Anchoring Vision and Language Knowledge for Weakly Supervise...

引用

2024 conference on visual communications and image processing

作者： Nugroho, Muhammad Adi Park, Jinyoung Kim, Donguk Kim, Changick Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (纸本)9798331529543;9798331529550

The emergence of Foundation Vision-Language Models (VLMs) has ignited a surge of research in the computer vision field due to their robust baseline performance. Inspired by this, we propose the Anchoring Vision-Language Network (AnViL-Net), which integrates a vision language model for the challenging task of Weakly-Supervised Group Activity Recognition (WSGAR). Our network effectively incorporates VLMs into WSGAR, addressing the challenges posed by dynamic actor motions and domain-specific activity classes. AnViL-Net leverages highly generalized VLM vision features as anchors for extracting visual features. Additionally, semantically meaningful VLM language features serve as anchors for inferring the semantic relationships between actors and their activities. We demonstrate the effectiveness of AnViL-Net on multiple group activity datasets, achieving competitive state-of-the-art results.

关键词： Group Activity Recognition visual Language Models

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Multi-QP Rate Distortion Optimized Quantization Using Deep Learning

Multi-QP Rate Distortion Optimized Quantization Using Deep L...

引用

2023 ieee international conference on visual communications and image processing, vcip 2023

作者： Afro, Pierre-Alain Strus, Loic Bonnaud, Laurent Caplier, Alice Robin, Frederic Allegro DVT France Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab France

ISBN: (纸本)9798350359855

RDOQ (Rate Distortion Optimized Quantization) is an efficient encoding tool that can be used with several codecs such as H.264/AVC, H.265/HEVC or AV1. Although this algorithm can significantly reduce the bit rate, its complexity is a limitation for video coding hardware solutions. Studies have succeeded in simplifying RDOQ allowing its integration into HM, the reference software implementation of H.265/HEVC. However, its iterative and sequential behavior does not allow an efficient hardware implementation. With the advent of machine learning, neural networks have been introduced to mimic the RDOQ algorithm in a parallel way. Previous proposed Deep-Learning based RDOQ frameworks still need to be trained for each Quantization Parameter (QP) which is a huge limitation for hardware implementation. To address this issue, we propose a Multi-QP Deep Learning based RDOQ, trained with data extracted from 4 QPs and achieving targeted performances with QP ranging from 22 to 37. Our multi-QP model almost reaches HM-RDOQ performance in terms of BD-Rate savings. © 2023 ieee.

关键词： image coding

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Aerial image Classification with Label Splitting and Optimized Triplet Loss Learning

Aerial Image Classification with Label Splitting and Optimiz...

引用

ieee international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Liao, Rijun Li, Zhu Bhattacharyya, Shuvra S. York, George Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA Univ Maryland Dept Elect & Comp Engn College Pk MD USA Univ Maryland UMIACS College Pk MD 20742 USA US Air Force Acad Dept Elect & Comp Engn Colorado Springs CO USA US Air Force Acad UAS Res Ctr Colorado Springs CO 80840 USA

ISBN: (纸本)9781728185514

With the development of airplane platforms, aerial image classification plays an important role in a wide range of remote sensing applications. The number of most of aerial image dataset is very limited compared with other computer vision datasets. Unlike many works that use data augmentation to solve this problem, we adopt a novel strategy, called, label splitting, to deal with limited samples. Specifically, each sample has its original semantic label, we assign a new appearance label via unsupervised clustering for each sample by label splitting. Then an optimized triplet loss learning is applied to distill domain specific knowledge. This is achieved through a binary tree forest partitioning and triplets selection and optimization scheme that controls the triplet quality. Simulation results on NWPU, UCM and AID datasets demonstrate that proposed solution achieves the state-of-the-art performance in the aerial image classification.

关键词： Aerial image Classification Label Splitting Semantic Label Appearance Label Optimized Triplet Loss

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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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Hybrid Representation for 4D Medical image Compression

Hybrid Representation for 4D Medical Image Compression

引用

2024 conference on visual communications and image processing

作者： Zheng, Wuyang Meng, Jiarui Zhang, Jiaqi Zhang, Jian Ma, Siwei Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Due to the substantial storage requirements of the 4D medical images, achieving efficient compression of such images is a crucial topic. Existing traditional image/video coding methods have achieved remarkable results in most compression tasks, but their performance in encoding 4D medical images remain poor. This is because these methods cannot fully exploit the spatio-temporal correlations in 4D images. Recently, implicit neural representation (INR) based image/video compression methods have made significant progress, with coding performance comparable to traditional methods. However, they also suffer from significant performance losses in 4D medical image compression like traditional methods. In this paper, we propose an efficient hybrid representation framework, which includes six learnable feature planes and a tiny MLP decoder. This framework alleviates the issue of previous methods lacking the ability to utilize the spatio-temporal correlations in 4D medical images, enabling it to capture these information more effectively. We also introduce a novel adaptive plane scaling strategy that allocates the numbers of parameter in each plane based on the resolution of the image. This design allows the model to further enhance the reconstruction quality at the same compression ratio. Extensive experiments show that our model achieves better RD performance compared to traditional and INR-based methods, and it also offers faster encoding speeds than INR-based methods.

关键词： Medical Data Compression Hybrid Representation Feature Planes

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KonIQ-10k-LT: Overcoming Score Priors in Blind image Quality Assessment Under Imbalanced Distributions

KonIQ-10k-LT: Overcoming Score Priors in Blind Image Quality...

引用

2024 conference on visual communications and image processing

作者： Yuan, Desen Wang, Lei Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Blind image Quality Assessment (BIQA) is essential in computational vision for predicting the visual quality of digital images without reference counterparts. Despite advancements through convolutional neural networks (CNNs), a significant challenge in BIQA remains the long-tail distribution of image quality scores, leading to biased training and reduced model generalization. To address this, we restructured the KonIQ-10k dataset to create an imbalanced version named KonIQ-10k-LT, manipulating the distribution of image quality scores to have opposing distributions in the training and validation sets. This restructuring increases the proportion of certain quality scores in the training set while decreasing them in the validation set. Experimental results show a significant performance decline of BIQA models on the KonIQ-10k-LT dataset compared to the original KonIQ-10k, highlighting the challenge posed by the long-tail distribution. To mitigate this issue, we propose a Proportion Weighted Balancing (PWB) method as a baseline, designed to enhance the robustness and generalization ability of BIQA models. Our findings demonstrate that the proposed WB method improves the performance and reliability of BIQA models under these challenging conditions.

关键词： Blind image quality assessment Long-tail learning

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Transformer-based Deep Frame Rate up Conversion for Hybrid High-Efficiency Video Coding

Transformer-based Deep Frame Rate up Conversion for Hybrid H...

引用

2023 ieee international conference on visual communications and image processing, vcip 2023

作者： Nguyen, Van Thang Hung Lu, Manh VinUniversity Viettel High Technology Industries Corporation College of Engineering and Computer Science Hanoi Viet Nam Viettel High Technology Industries Corporation Camera Center Hanoi Viet Nam

ISBN: (纸本)9798350359855

Mainstream state-of-The-Art video compression standards such as High-Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are block-based coding. On the other hand, neural video compression networks have obtained impressive results recently. However, neural video compression is not practical for use due to its high computations. In this paper, we propose a hybrid approach that exploits a deep frame rate up-conversion network to improve the coding efficiency of existing video codecs such as HEVC. We analyze a coding scenario of a combination between the Frame Rate Up-Conversion (FRUC) network and the random access coding configuration. In addition, we show that the FRUC network trained on compressed images outperforms the model trained on non-compressed images for the video frame interpolation task. The proposed framework skips encoding odd frames in the display order of original video sequences at the encoder side and reconstructs them at the decoder side via the FRUC network. Experimental results show the proposed framework improves the coding efficiency of HEVC 4.2 % for random access configuration. © 2023 ieee.

关键词： image compression

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A LIGHT-WEIGHT HEVC ENCODER FOR image CODING

A LIGHT-WEIGHT HEVC ENCODER FOR IMAGE CODING

引用

ieee international conference on visual communications and image processing (vcip)

作者： Liang, Fei Peng, Xiulian Xu, Jizheng Univ Sci & Tech China MSRA MOE Joint Key Lab Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781479902880

High Efficiency Video Coding (HEVC), not only provides a much better coding efficiency than previous video coding standards, but also shows significantly superior performance than other image coding schemes when applied to image coding. However, the improvement is at the cost of significant increase of encoding complexity. In this paper, we focus on retaining the high coding efficiency provided by HEVC while largely reducing its encoding complexity for image coding. By applying various techniques including optimized coding structure parameters, coding unit early termination, fast intra prediction and transform skip mode decision, we significantly reduce the complexity of HEVC intra coding while keeping most of its coding efficiency. Experimental results show that our light-weight HEVC encoder can save about 82% coding time compared with original HEVC encoder. With a slight loss to the HEVC reference software, the proposed scheme still gains about 19% in BD-BR compared with H.264/AVC.

关键词： HEVC image coding intra-frame coding

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