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检索条件"任意字段=Conference on Visual Communications and Image Processing 2004"

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Semantic Attribute Guided image Aesthetics Assessment

Semantic Attribute Guided Image Aesthetics Assessment

引用

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

作者： Duan, Jiachen Chen, Pengfei Li, Leida Wu, Jinjian Shi, Guangming Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781665475921

image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, object emphasis, etc. However, the state-of-the-art IAA approaches usually follow the data-driven framework without considering the rich attributes contained in images. With this motivation, this paper presents a new semantic attribute guided IAA model, where the attention maps of semantic attributes are employed to enhance the representation ability of general aesthetic features for more effective aesthetics assessment. Specifically, we first design an attribute attention generation network to obtain the attention maps for different semantic attributes, which are utilized to weight the general aesthetic features, producing the semantic attribute-enhanced feature representations. Then, the Graph Convolutional Network (GCN) is employed to further investigate the inherent relationship among the enhanced aesthetic features, producing the final image aesthetics prediction. Extensive experiments and comparisons on three public IAA databases demonstrate the effectiveness of the proposed method.

关键词： image aesthetics assessment semantic attributes aesthetic reasoning graph convolutional network

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Y/C separation of composite color video signals using samples with non-opposite sub-carrier phases

Y/C separation of composite color video signals using sample...

引用

conference on visual communications and image processing 2004

作者： Cordes, N de Haan, G Philips Res Labs Video Proc & Visual Percept Grp Eindhoven Netherlands

ISBN: (纸本)0819452114

For analog color television standards such as PAL and NTSC, the transmission of color (C) takes place within the band available for the luminance (Y). At the television receiver, the required separation of Y and C can only be imperfect as both components now share the same frequency space. Modern televisions apply so-called combfilters that exploit the opposite sub-carrier phase of correlated samples to separate both components. However, cross-talk artifacts and loss of resolution will occur in situations where no sufficiently correlated samples meet the strict opposite phase requirement. In this paper, a novel Y/C separation method is presented that is able to use samples with non-opposite sub-carrier phases.

关键词： luminance chrominance separation y/c separation comb filters composite video video signal processing

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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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Mask-Guided Transformer for Human-Object Interaction Detection

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

引用

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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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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Progressive compression of PointTexture images

Progressive compression of PointTexture images

引用

conference on visual communications and image processing 2004

作者： Song, IW Kim, CS Lee, SU Seoul Natl Univ Signal Proc Lab Sch Elect Engn & Comp Sci Seoul South Korea

ISBN: (纸本)0819452114

In this paper, we develop a tree-structured predictive partial matching (PPM) scheme for progressive compression of PointTexture images. By incorporating PPM with tree-structured coding, the proposed algorithm can compress 3D depth information progressively into a single bitstream. Also, the proposed algorithm compresses color information using a differential pulse coding modulation (DPCM) coder and interweaves the compressed depth and color information efficiently. Thus, the decoder can reconstruct 3D models from the coarsest resolution to the highest resolution from a single bitstream. Simulation results demonstrate that the proposed algorithm provides much better compression performance than a universal Lempel-Ziv coder, WinZip.

关键词： 3D compression progressive compression octree structure layered depth images Point-Texture depth image based rendering

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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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