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Cross-Device image Saliency Detection: Database and Comparative Analysis

Cross-Device Image Saliency Detection: Database and Comparat...

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2024 conference on visual communications and image processing

作者： Ding, Xiaoying Yue, Guanghui Zhang, Yingxue Zhongnan Univ Econ & Law Sch Informat Engn Wuhan Peoples R China Shenzhen Univ Med Sch Sch Biomed Engn Shenzhen Peoples R China Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

While saliency detection for images has been extensively studied during the past decades, only a little work explores the influence of different viewing devices (i.e., tablet computer, mobile phone) towards human visual attention behavior. The lack of research in this field hinders the research progress in cross-device image saliency detection. In this paper, we first establish a novel cross-device saliency detection (CDSD) database based on eye-tracking experiments and investigate subjects' visual attention behavior when using different viewing devices. Then, we evaluate several classic saliency detection models using the CDSD database and the evaluation results indicate that the cross-device performance of these models need further improvement. Finally, some meaningful discussions are provided which might enlighten the design of cross-device saliency detection model. The proposed CDSD database will be made publicly available.

关键词： cross-device saliency detecton visual attention eye-tracking

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Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized image Compression

Spatial-Channel Context-Based Entropy Modeling for End-to-en...

引用

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

作者： Li, Chongxin Luo, Jixiang Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728180687

End-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-end optimized methods for image compression.

关键词： End-to-end optimized image compression context-based entropy coding artificial neural networks

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No-Reference Stereoscopic image Quality Assessment Considering Multi-loss Constraints

No-Reference Stereoscopic Image Quality Assessment Consideri...

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

作者： Han, Yongtian Li, Sumei Yue, Guanghui Chang, Yongli Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Shenzhen Peoples R China

ISBN: (纸本)9781728180687

In this paper, a three-channel convolutional neural network (CNN) constrained by multiple loss functions is designed for stereoscopic image quality assessment (SIQA). Given that both monocular and binocular information are crucial for SIQA, we take the patches of left images, right images and difference images as the inputs of the three channels respectively. Since using the ground truth as the labels of image patches cannot accurately characterize their quality, we propose to individually label each image patch to preserve the quality difference among different regions and views. Moreover, the multi-loss structure is adopted in the proposed method to consider both local features and global features simultaneously, which can constrain the feature learning from multiple perspectives. And the additional adaptive loss weights make the multi-loss network more flexible and universal. The experimental results show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.

关键词： stereoscopic image quality assessment multi-loss proxy labels adaptive loss weights convolutional neural network

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NIR image Colorization Using SPADE Generator and Grayscale Approximated Self-Reconstruction

NIR Image Colorization Using SPADE Generator and Grayscale A...

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

作者： Sun, Tian Jung, Cheolkon Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9781728180687

Near infrared (NIR) images are robust to ambient light and contain clear textures in low light condition. In this paper, we propose NIR image colorization using spatial adaptive denormalization (SPADE) generator and grayscale approximated self-reconstruction. Compared with traditional image to image translation methods, the proposed NIR colorization pursues photorealism rather than generative diversity. The challenge of this task is NIR-RGB mis-registration in training data. We address this problem by separately extracting NIR texture and RGB color with an end to end SPADE based model. Moreover, the proposed method facilitates a more precise synthesis with a given low light RGB reference image. Experiments on an open NIR-RGB dataset verify that the proposed method effectively preserves NIR textures and RGB colors in the synthesized results and outperforms the baselines in terms of visual quality and quantitative assessments.

关键词： NIR colorization mis-registered data deep learning generative adversarial networks multi-modal synthesis

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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...

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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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Mask-Guided Transformer for Human-Object Interaction Detection

Mask-Guided Transformer for Human-Object Interaction Detecti...

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

作者： Ying, Daocheng Yang, Hua Sun, Jun Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Inst Image Commun & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665475921

Human-object interaction (HOI) detection is a meaningful research topic on human activity understanding. Recent works have made significant progress by focusing on efficient triplet matching and leveraging image-wide features based on encoder-decoder architecture. However, the ability to gather relevant contextual information about human is limited and different sub-tasks in HOI detection are not differentiated by specific decoupling in previous methods. To this end, we propose a new transformer-based method for HOI detection, namely, Mask-Guided Transformer (MGT). Our model, which is composed of five parallel decoders with a shared encoder, not only emphasizes interactive regions by applying body features, but also disentangles the prediction of instance and interaction. We achieve a favorable result at 63.3 mAP on the well-known HOI detection dataset V-COCO.

关键词： Transformers Object detection Human and object interaction HOI detection

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Frequency-Aware Hierarchical image Compression for Humans and Machines

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

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

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

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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： 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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End-to-end image Compression with Swin-Transformer

End-to-end Image Compression with Swin-Transformer

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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...

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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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