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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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Multiscale Vision Transformer with Deep Clustering-Guided Refinement for Weakly Supervised Object Localization

Multiscale Vision Transformer with Deep Clustering-Guided Re...

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2023 ieee International Conference on visual communications and image processing, vcip 2023

作者： Kim, David Minkwan Cha, Sinhae Kang, Byeongkeun Hanyang University Department of Computer Science Seoul Korea Republic of Seoul National University of Science and Technology Department of Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

This work addresses the task of weakly-supervised object localization. The goal is to learn object localization using only image-level class labels, which are much easier to obtain compared to bounding box annotations. This task is important because it reduces the need for labor-intensive ground-Truth annotations. However, methods for object localization trained using weak supervision often suffer from limited accuracy in localization. To address this challenge and enhance localization accuracy, we propose a multiscale object localization transformer (MOLT). It comprises multiple object localization transformers that extract patch embeddings across various scales. Moreover, we introduce a deep clustering-guided refinement method that further enhances localization accuracy by utilizing separately extracted image segments. These segments are obtained by clustering pixels using convolutional neural networks. Finally, we demonstrate the effectiveness of our proposed method by conducting experiments on the publicly available ILSVRC-2012 dataset. © 2023 ieee.

关键词： image enhancement

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A Near Lossless Learned image Coding Network Quantization Approach for Cross-Platform Inference

A Near Lossless Learned Image Coding Network Quantization Ap...

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2023 ieee International Conference on visual communications and image processing, vcip 2023

作者： Hang, Xinyu Jia, Chuanmin Ma, Siwei Gao, Wen Peking University National Engineering Research Center on Visual Technology School of Computer Science China Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350359855

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios. © 2023 ieee.

关键词： Table lookup

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Hybrid Implicit Neural image Compression with Subpixel Context Model and Iterative Pruner

Hybrid Implicit Neural Image Compression with Subpixel Conte...

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2023 ieee International Conference on visual communications and image processing, vcip 2023

作者： Li, Hangyu Jiang, Wei Li, Litian Zhai, Yongqi Wang, Ronggang Peking University Shenzhen Graduate School Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798350359855

Recently, image compression based on Implicit Neural Representation (INR) has gained interest. However, the performance of INR-based methods is poor because MLP can not fit high-frequency information well. Recently, a kind of hybrid INR method was proposed, which transmits not only the parameters of MLP but also some external latents to help to overfit at the decoder. Despite its low MACs (Multiply-Accumulate Operations per pixel), the decoding time is still slow due to the serial autoregressive context model for entropy coding. Hence, we propose two methods to improve the hybrid INR method's decoding speed and performance. First, the Subpixel Context Model (SAM) is aimed to speed up the process of entropy coding. The proposed SAM is latent by latent instead of pixel by pixel. Second, we add an iterative pruner to the pipeline of hybrid INR compression, to further reduce the bit rate. The performance and MACs of our approach are on par with the SOTA of the hybrid INR methods. The decoding speed is about eight times faster on CPU, and three times faster on GPU. © 2023 ieee.

关键词： image compression

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Two-stage Parallax Correction and Multi-stage Cross-view Fusion Network Based Stereo image Super-Resolution

Two-stage Parallax Correction and Multi-stage Cross-view Fus...

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

作者： Zheng, Yijian Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the two major problems of the existing methods are that the parallax correction is insufficient and the cross-view information fusion only occurs in the beginning of the network. To address these problems, we propose a two-stage parallax correction and a multi-stage cross-view fusion network for better stereo image SR results. Specially, the two-stage parallax correction module consists of horizontal parallax correction and refined parallax correction. The first stage corrects horizontal parallax by parallax attention. The second stage is based on deformable convolution to refine horizontal parallax and correct vertical parallax simultaneously. Then, multiple cascaded enhanced residual spatial feature transform blocks are developed to fuse cross-view information at multiple stages. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI2012, KITTI2015, Middlebury and Flickr1024 datasets.

关键词： Stereo image super-resolution Parallax correction Multi-stage cross-view fusion

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Mining Larger Class Activation Map with Common Attribute Labels

Mining Larger Class Activation Map with Common Attribute Lab...

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ieee International Conference on visual communications and image processing (vcip)

作者： Zhang, Runtong Meng, Fanman Li, Hongliang Wu, Qingbo Ngan, King N. Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728180687

Class Activation Map (CAM) is the visualization of target regions generated from classification networks. However, classification network trained by class-level labels only has high responses to a few features of objects and thus the network cannot discriminate the whole target. We think that original labels used in classification tasks are not enough to describe all features of the objects. If we annotate more detailed labels like class-agnostic attribute labels for each image, the network may be able to mine larger CAM. Motivated by this idea, we propose and design common attribute labels, which are lower-level labels summarized from original image-level categories to describe more details of the target. Moreover, it should be emphasized that our proposed labels have good generalization on unknown categories since attributes (such as head, body, etc.) in some categories (such as dog, cat, etc.) are common and class-agnostic. That is why we call our proposed labels as common attribute labels, which are lower-level and more general compared with traditional labels. We finish the annotation work based on the PASCAL VOC2012 dataset and design a new architecture to successfully classify these common attribute labels. Then after fusing features of attribute labels into original categories, our network can mine larger CAMs of objects. Our method achieves better CAM results in visual and higher evaluation scores compared with traditional methods.

关键词： image classification class activation map common attribute label

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A Real-Time Global Inference Network for One-Stage Referring Expression Comprehension

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ieee Transactions on Neural Networks and Learning Systems 2023年第1期34卷 134-143页

作者： Zhou, Yiyi Ji, Rongrong Luo, Gen Sun, Xiaoshuai Su, Jinsong Ding, Xinghao Lin, Chia-Wen Tian, Qi Xiamen University Media Analytics and Computing Laboratory Xiamen361005 China Xiamen University School of Informatics Informatics and Communication Engineering Department Xiamen361005 China School of Informatics Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China Xiamen University School of Informatics Xiamen361005 China National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Huawei Technologies Company Ltd. Cloud BU Shenzhen518129 China

Referring expression comprehension (REC) is an emerging research topic in computer vision, which refers to the detection of a target region in an image given a test description. Most existing REC methods follow a multistage pipeline, which is computationally expensive and greatly limits the applications of REC. In this article, we propose a one-stage model toward real-time REC, termed real-time global inference network (RealGIN). RealGIN addresses the issues of expression diversity and complexity of REC with two innovative designs: adaptive feature selection (AFS) and Global Attentive ReAsoNing (GARAN). Expression diversity concerns varying expression content, which includes information such as colors, attributes, locations, and fine-grained categories. To address this issue, AFS adaptively fuses features of different semantic levels to tackle the changes in expression content. In contrast, expression complexity concerns the complex relational conditions in expressions that are used to identify the referent. To this end, GARAN uses the textual feature as a pivot to collect expression-aware visual information from all regions and then diffuses this information back to each region, which provides sufficient context for modeling the relational conditions in expressions. On five benchmark datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, ReferIT, and Flickr30k, the proposed RealGIN outperforms most existing methods and achieves very competitive performances against the most advanced one, i.e., MAttNet. More importantly, under the same hardware, RealGIN can boost the processing speed by 10-20 times over the existing methods. © 2012 ieee.

关键词： Semantics

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Two-stage Parallax Correction and Multi-stage Cross-view Fusion Network Based Stereo image Super-Resolution

Two-stage Parallax Correction and Multi-stage Cross-view Fus...

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ieee visual communications and image processing (vcip)

作者： Yijian Zheng Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

关键词： Fuses visual communication Convolution Superresolution Transforms Benchmark testing Spatial resolution

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2012 ieee visual communications and image processing, vcip 2012

2012 IEEE Visual Communications and Image Processing, VCIP 2...

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2012 ieee visual communications and image processing, vcip 2012

ISBN: (纸本)9781467344050

The proceedings contain 131 papers. The topics discussed include: gradient-based fast decision for intra prediction in HEVC;adaptive rate control for high efficiency video coding;uniform probability model for deriving intra prediction angular table in high efficiency video coding;intra prediction based on statistical modeling of images;SEQM: edge quality assessment based on structural pixel matching;QoE analysis for scalable video adaptation;assessing photo quality with geo-context and crowdsourced photos;image registration by using a descriptor for repetitive patterns;a canonical correlation analysis based motion model for probabilistic visual tracking;a patch-based framework for detecting abnormal activities with a PTZ camera;generalized MMSD feature extraction using QR decomposition;affine skip and direct modes for efficient video coding;and compound image compression by multi-stage prediction.

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A New Bounding Box based Pseudo Annotation Generation Method for Semantic Segmentation

A New Bounding Box based Pseudo Annotation Generation Method...

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ieee visual communications and image processing (vcip)

作者： Xiaolong Xu Fanman Meng Hongliang Li Qingbo Wu King Ngi Ngan Shuai Chen School of ICE University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This paper proposes a fusion-based method to generate pseudo-annotations from bounding boxes for semantic segmentation. The idea is to first generate diverse foreground masks by multiple bounding box segmentation methods, and then combine these masks to generate pseudo-annotations. Existing methods generate foreground masks from bounding boxes by classical segmentation methods driving by low-level features and own local information, which is hard to generate accurate and diverse results for the fusion. Different from the traditional methods, multiple class-agnostic models are modeled to learn the objectiveness cues by using existing labeled pixel-level annotations and then to fuse. Firstly, the classical Fully Convolutional Network (FCN) that densely predicts the pixels' labels is used. Then, two new sparse prediction based class-agnostic models are proposed, which simplify the segmentation task as sparsely predicting the boundary points through predicting the distance from the bounding box border to the object boundary in Cartesian Coordinate System and the Polar Coordinate System, respectively. Finally, a voting-based strategy is proposed to combine these segmentation results to form better pseudo-annotations. We conduct experiments on PASCAL VOC 2012 dataset. The mIoU of the proposed method is 68.7%, which outperforms the state-of-the-art method by 1.9%.

关键词： Annotations image segmentation Training Semantics Predictive models Task analysis Pipelines

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