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检索条件"任意字段=2024 Conference on Visual Communications and Image Processing"

共 6941 条记录，以下是271-280 订阅

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Codec-agnostic Lenslet Video Coding with Smoothing Transform

Codec-agnostic Lenslet Video Coding with Smoothing Transform

引用

2024 conference on visual communications and image processing

作者： Soetens, Eline Lafruit, Gauthier Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium

ISBN: (纸本)9798331529543;9798331529550

Plenoptic cameras are light field capturing devices able to acquire large amounts of angular and spatial information. The lenslet video produced by such cameras presents on each frame a distinctive hexagonal pattern of micro-images. Due to the particular structure of lenslet images, traditional video codecs perform poorly on lenslet video. Previous works have proposed a preprocessing scheme that cuts and realigns the micro-images on each lenslet frame. While effective, this method introduces high frequency components into the processed image. In this paper, we propose an additional step to the aforementioned scheme by applying an invertible smoothing transform. We evaluate the enhanced scheme on lenslet video sequences captured with single-focused and multi-focused plenoptic cameras. On average, the enhanced scheme achieves 9.85% bitrate reduction compared to the existing scheme.

关键词： Video signal processing

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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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Cross-Device image Saliency Detection: Database and Comparative Analysis

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

引用

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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Perceptual image Compression With Conditional Diffusion Transformers

Perceptual Image Compression With Conditional Diffusion Tran...

引用

2024 conference on visual communications and image processing

作者： Mao, Rui Feng, Xinmin Gao, Changsheng Li, Li Liu, Dong Sun, Xiaoyan Univ Sci & Technol China MOE Key Lab Brain Inspired Intelligent Percept & Hefei Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Generative models have significantly advanced generative AI, particularly in image and video generation. Recognizing their potential, researchers have begun exploring their application in image compression. However, existing methods face two primary challenges: limited performance improvement and high model complexity. In this paper, to address these two challenges, we propose a perceptual image compression solution by introducing a conditional diffusion model. Given that compression performance heavily depends on the decoder's generative capability, we base our decoder on the diffusion transformer architecture. To address the model complexity problem, we implement the diffusion transformer architecture with Swin transformer. Equipped with enhanced generative capability, we further augment the decoder with informative features using a multi-scale feature fusion module. Experimental results demonstrate that our approach surpasses existing perceptual image compression methods while achieving lower model complexity.

关键词： Diffusion transformer (DiT) image generation perceptual image compression Swin transformer

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ALICE: Adapt your Learnable image Compression modEl for variable bitrates

ALICE: Adapt your Learnable Image Compression modEl for vari...

引用

2024 conference on visual communications and image processing

作者： Spadaro, Gabriele Ali, Muhammad Salman Presta, Alberto Pilo, Giommaria Bae, Sung-Ho Giraldo, Jhony H. Fiandrotti, Attilio Grangetto, Marco Tartaglione, Enzo Inst Polytech Paris Telecom Paris LTCI Paris France Univ Turin Turin Italy Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798331529543;9798331529550

When training a Learned image Compression model, the loss function is minimized such that the encoder and the decoder attain a target Rate-Distorsion trade-off. Therefore, a distinct model shall be trained and stored at the transmitter and receiver for each target rate, fostering the quest for efficient variable bitrate compression schemes. This paper proposes plugging Low-Rank Adapters into a transformer-based pre-trained LIC model and training them to meet different target rates. With our method, encoding an image at a variable rate is as simple as training the corresponding adapters and plugging them into the frozen pre-trained model. Our experiments show performance comparable with state-of-the-art fixed-rate LIC models at a fraction of the training and deployment cost. We publicly released the code at https://***/EIDOSLAB/ALICE.

关键词： image compression

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Lookup Register-Tables with Interpolation for Effective image Transformation on x86/64 CPUs

Lookup Register-Tables with Interpolation for Effective Imag...

引用

2024 conference on visual communications and image processing

作者： Kamei, Hirokazu Honda, Soichiro Hayashi, Kohei Maeda, Yoshihiro Fukushima, Norishige Nagoya Inst Technol Grad Sch Engn Nagoya Aichi Japan Shibaura Inst Technol Coll Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Lookup tables (LUTs) are commonly used to speed up image processing by handling complex mathematical functions like sine and exponential calculations. They are used in various applications such as camera image processing, high-dynamic range imaging, and edge-preserving filtering. However, due to the increasing gap between computing and input/output performance, LUTs are becoming less effective. Even though specific circuits like SIMD can improve LUT efficiency, they still need to bridge the performance gap fully. The gap makes it difficult to choose between direct numerical and LUT calculations. For this problem, a register-LUTs method with the nearest neighbor was proposed;however, it is limited for functions with narrow-range values approaching zero. In this paper, we propose a method for using register LUTs to process images efficiently over a wide range of values. Our contributions include proposing register-LUT with linear interpolation for efficient computation, using a smaller data type for further efficiency, and suggesting an efficient data retrieving method.

关键词： SIMD register-LUT interpolation x86/64 CPU image processing

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Learned image Compression with Quantization Error Compensator

Learned Image Compression with Quantization Error Compensato...

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2024 conference on visual communications and image processing

作者： Fu, Nianxiang Chen, Zhenzhong Wang, Huairui Liu, Shan Wuhan Univ Hubei Luojia Lab Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Shenzhen Peoples R China Tencent Amer Palo Alto CA USA

ISBN: (纸本)9798331529543;9798331529550

Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.

关键词： Learned image compression quantization error compensator quantization error

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Light field depth estimation based on fusion of multi-scale semantic and geometric information

Light field depth estimation based on fusion of multi-scale ...

引用

2024 conference on visual communications and image processing

作者： Tan, Feng Deng, Huiping Xiang, Sen Wu, Jin Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Depth estimation of light field images is a crucial technique in various applications, including 3D reconstruction, autonomous driving, and object tracking. However, current deep-learning methods ignore the geometric information of the light field image and are limited to learning repetitive textures, which leads to inaccurate estimates of depth. The paper proposes a light field depth estimation network that fuses multi-scale semantic information with geometric information to address the problem of non-adaptation for repeated texture regions. The main focus of the network is the semantic and geometric information fusion (SGI) module, which can adaptively combine semantic and geometric information to improve the efficiency of cost aggregation. Furthermore, SGI module establishes a direct link between feature extraction and cost aggregation, providing feedback for feature extraction and guiding more efficient feature extraction. The experimental results from the optical field synthesis dataset HCI 4D demonstrate that the method has high accuracy and generalisation performance.

关键词： light field image Depth estimation Repeat texture area Semantic and geometric information fusion

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Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural image Compression

Semantic Ensemble Loss and Latent Refinement for High-Fideli...

引用

2024 conference on visual communications and image processing

作者： Li, Daxin Bai, Yuanchao Wang, Kai Jiang, Junjun Liu, Xianming Harbin Inst Technol Fac Comp Harbin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually displeasing artifacts, such as blurring, color shifting, and texture loss, thereby compromising perceptual quality of images. To address these issues, this study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss, to enhance the perceptual quality of image reconstructions. Additionally, we have implemented a latent refinement process to generate content-aware latent codes. These codes adhere to bit-rate constraints, and prioritize bit allocation to regions of greater importance. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.

关键词： Generative image Compression Learned image Compression

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image Forensics Strikes Back: Defense Against Adversarial Patch

Image Forensics Strikes Back: Defense Against Adversarial Pa...

引用

2024 conference on visual communications and image processing

作者： Kao, Ching-Chia Lu, Chun-Shien Yu, Chia-Mu Acad Sinica Inst Informat Sci IIS Taipei Taiwan Natl Taiwan Univ Taipei Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9798331529543;9798331529550

Traffic sign recognition plays a crucial role in self-driving cars, but unfortunately, it is vulnerable to adversarial patches (AP). Although AP can efficiently fool DNN-based models in previous studies, the connection between image forensics and AP detection still needs to be explored. From a high-level point of view, their goals are the same. That is to find tampered regions and prevent false positives in the meantime. A natural question arises: "Is achieving application-agnostic anomaly detection possible?" In this paper, we propose image Forensics Defense Against Adversarial Patch (IDAP), a framework to defend against adversarial patches via generalizable features learned from tampered images. In addition, we incorporate the Hausdorff erosion loss into our network model for joint training to complete the shape of a predicted mask. Extensive experimental comparisons on three datasets, including COCO, DFG, and APRICOT demonstrate that IDAP outperforms state-of-the-art AP detection methods.

关键词： Adversarial Patch AI Security image Forensics

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