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FREQ-MIP-AA: Frequency Mip Representation for Anti-Aliasing Neural Radiance Fields

FREQ-MIP-AA: Frequency Mip Representation for Anti-Aliasing ...

引用

IEEE International conference on image processing

作者： Youngin Park Seungtae Nam Cheul-Hee Hahm Eunbyung Park Department of Digital Media and Communications Engineering Sungkyunkwan University Visual Display Division Samsung Electronics Department of Artificial Intelligence Sungkyunkwan University Department of Electrical and Computer Engineering Sungkyunkwan University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Neural Radiance Fields (NeRF) have shown remarkable success in representing 3D scenes and generating novel views. However, they often struggle with aliasing artifacts, especially when rendering images from different camera distances from the training views. To address the issue, Mip-NeRF proposed using volumetric frustums to render a pixel and suggested integrated positional encoding (IPE). While effective, this approach requires long training times due to its reliance on MLP architecture. In this work, we propose a novel antialiasing technique that utilizes grid-based representations, usually showing significantly faster training time. In addition, we exploit frequency-domain representation to handle the aliasing problem inspired by the sampling theorem. The proposed method, FreqMipAA, utilizes scale-specific low-pass filtering (LPF) and learnable frequency masks. Scale-specific low-pass filters (LPF) prevent aliasing and prioritize important image details, and learnable masks effectively remove problematic high-frequency elements while retaining essential information. By employing a scale-specific LPF and trainable masks, FreqMipAA can effectively eliminate the aliasing factor while retaining important details. We validated the proposed technique by incorporating it into a widely used grid-based method. The experimental results have shown that the FreqMipAA effectively resolved the aliasing issues and achieved state-of-the-art results in the multi-scale Blender dataset. Our code is available at https://***/yi0109/FreqMipAA.

关键词： Training image coding Three-dimensional displays Codes Filtering Frequency-domain analysis Low-pass filters

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Quality of Experience Evaluation for Streaming Video Using CGNN

Quality of Experience Evaluation for Streaming Video Using C...

引用

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

作者： Zhou, Zhiming Dong, Yu Song, Li Xie, Rong Li, Lin Zhou, Bing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728180687

One of the principal contradictions these days in the field of v ideo is l ying b etween t he b ooming d emand for evaluating the streaming video quality and the low precision of the Quality of Experience prediction results. In this paper, we propose Convolutional Neural Network and Gate Recurrent Unit (CGNN)-QoE, a deep learning QoE model, that can predict overall and continuous scores of video streaming services accurately in real time. We further implement state-of-the-art models on the basis of their works and compare with our method on six public available datasets. In all considered scenarios, the CGNN-QoE outperforms existing methods.

关键词： Quality of Experience (QoE) adaptive video streaming Gate Recurrent Unit

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

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

引用

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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DEN: Disentanglement and Enhancement Networks for Low Illumination images

DEN: Disentanglement and Enhancement Networks for Low Illumi...

引用

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

作者： Bow, Nelson Chong Ngee Vu-Hoang Tran Kerdsiri, Punchok Loh, Yuen Peng Huang, Ching-Chun Multimedia Univ Fac Comp & Informat Cyberjaya Selangor Malaysia Ho Chi Minh City Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam PTT Explorat & Prod PCL DCOE Digital Ctr Excellence Bangkok Thailand Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728180687

Though learning-based low-light enhancement methods have achieved significant success, existing methods are still sensitive to noise and unnatural appearance. The problems may come from the lack of structural awareness and the confusion between noise and texture. Thus, we present a low-light image enhancement method that consists of an image disentanglement network and an illumination boosting network. The disentanglement network is first used to decompose the input image into image details and image illumination. The extracted illumination part then goes through a multi-branch enhancement network designed to improve the dynamic range of the image. The multi-branch network extracts multi-level image features and enhances them via numerous subnets. These enhanced features are then fused to generate the enhanced illumination part. Finally, the denoised image details and the enhanced illumination are entangled to produce the normal-light image. Experimental results show that our method can produce visually pleasing images in many public datasets

关键词： low-light enhancement image disentanglement multi-branch enhancement network

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

No-Reference Stereoscopic Image Quality Assessment Consideri...

引用

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

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

引用

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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Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation

Hierarchical Reinforcement Learning Based Video Semantic Cod...

引用

IEEE visual communications and image processing (VCIP)

作者： Guangqi Xie Xin Li Shiqi Lin Zhibo Chen Li Zhang Kai Zhang Yue Li Unviersity of Science and Technology of China Hefei China ByteDance Inc.

ISBN: (纸本)9781665475938

The rapid development of intelligent tasks, e.g., segmentation, detection, and classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot be optimized in an end-to-end manner. To solve this problem, some pioneering works have applied reinforcement learning to implement image-wise semantic compression. Nevertheless, the video semantic compression has not been explored since its complex reference architectures and compression modes. In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC. Specifically, to simplify the complex mode decision of video semantic coding, we divided the action space into frame-level and CTU-level spaces in a hierarchical manner, and then explore the best mode selection for them progressively with the cooperation of frame-level and CTU-level agents. Moreover, since the modes of video semantic coding will exponentially increase with the number of frames in a Group of Pictures (GOP), we carefully investigate the effects of different mode selections for video semantic coding, and design a simple but effective mode simplification strategy for it. We have validated our HRLVSC on video segmentation task with HEVC reference software HM16.19. Extensive experimental results demonstrated that our HRLVSC can achieve over 39% BD-rate saving for video semantic coding under the Low Delay P configuration.

关键词： Video coding Training image segmentation image coding visual communication Semantics Reinforcement learning

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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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A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

引用

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

作者： Yan, Longbin Wang, Xiuheng Zhao, Min Liu, Shumin Chen, Jie Northwestern Polytech Univ Sch Marine Sci & Technol CIAIC Xian Peoples R China Shanghai Shengyao Intelligent Technol Co Ltd Shanghai Peoples R China Blueye Intelligence Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728180687

Near-infrared (NIR) images provide spectral information beyond the visible light spectrum and thus are useful in many applications. However, single-channel NIR images contain less information per pixel than RGB images and lack visibility for human perception. Transforming NIR images to RGB images is necessary for performing further analysis and computer vision tasks. In this work, we propose a novel NIR-to-RGB translation method. It contains two sub-networks and a fusion operator. Specifically, a U-net based neural network is used to learn the texture information while a CycleGAN based neural network is adopted to excavate the color information. Finally, a guided filter based fusion strategy is applied to fuse the outputs of these two neural networks. Experiment results show that our proposed method achieves superior NIR-to-RGB translation performance.

关键词： NIR-to-RGB translation U-net CycleGAN guided filter image fusion

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