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

共 4337 条记录，以下是161-170 订阅

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Entropy-based Deep Product Quantization for visual Search and Deep Feature Compression

Entropy-based Deep Product Quantization for Visual Search an...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Niu, Benben Wei, Ziwei He, Yun Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728185514

With the emergence of various machine-to-machine and machine-to-human tasks with deep learning, the amount of deep feature data is increasing. Deep product quantization is widely applied in deep feature retrieval tasks and has achieved good accuracy. However, it does not focus on the compression target primarily, and its output is a fixed-length quantization index, which is not suitable for subsequent compression. In this paper, we propose an entropy-based deep product quantization algorithm for deep feature compression. Firstly, it introduces entropy into hard and soft quantization strategies, which can adapt to the codebook optimization and codeword determination operations in the training and testing processes respectively. Secondly, the loss functions related to entropy are designed to adjust the distribution of quantization index, so that it can accommodate to the subsequent entropy coding module. Experimental results carried on retrieval tasks show that the proposed method can be generally combined with deep product quantization and its extended schemes, and can achieve a better compression performance under near lossless condition.

关键词： deep feature deep product quantization entropy image retrieval semi-supervised

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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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Automatic. video object detection and mask signal removal for efficient video pre-processing

Automatic. video object detection and mask signal removal fo...

引用

conference on visual communications and image processing 2004

作者： He, ZH Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

ISBN: (纸本)0819452114

In this work, we consider a generic definition of video object, which is a group of pixels with temporal motion coherence. The generic video object (GVO) is the superset of the conventional video objects discussed in the literature. Because of its motion coherence, the GVO can be easily recognized by the human visual system. However, due to its arbitray spatial distribution, the GVO cannot be easily detected by the existing algorithms which often assume the spatial homogeneousness of the video objects. In this work, we introduce the concept of extended optical flow and develop a dynamic programming framework for the GVO detection. Using this mathematical optimization formulation, whose solution is given by the the Viterbi algorithm, the proposed object detection algorithm is able to discover the motion path of the GVO automatically and refine its spatial location progressively. We apply the GVO detection algorithm to extract and remove the so-called "video mask" signals in the video sequence. Our experimental results show that this type of vision-guided video pre-processing significantly improves the compression efficiency.

关键词： Video Video compression Detection and tracking algorithms Signal detection Computer programming Optical flow Motion estimation Video processing image segmentation Binary data

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Generative DNA: Representation Learning for DNA-based Approximate image Storage

Generative DNA: Representation Learning for DNA-based Approx...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Franzese, Giulio Yan, Yiqing Serra, Giuseppe D'Onofrio, Ivan Appuswamy, Raja Michiardi, Pietro EURECOM Dept Data Sci Biot France

ISBN: (纸本)9781728185514

Synthetic DNA has received much attention recently as a long-term archival medium alternative due to its high density and durability characteristics. However, most current work has primarily focused on using DNA as a precise storage medium. In this work, we take an alternate view of DNA. Using neural-network-based compression techniques, we transform images into a latent-space representation, which we then store on DNA. By doing so, we transform DNA into an approximate image storage medium, as images generated back from DNA are only approximate representations of the original images. Using several datasets, we investigate the storage benefits of approximation, and study the impact of DNA storage errors (substitutions, indels, bias) on the quality of approximation. In doing so, we demonstrate the feasibility and potential of viewing DNA as an approximate storage medium.

关键词： DNA storage representation learning approximate storage compression

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Analyzing Time Complexity of Practical Learned image Compression Models

Analyzing Time Complexity of Practical Learned Image Compres...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Pan, Xiaohan Guo, Zongyu Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China

ISBN: (纸本)9781728185514

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.

关键词： Time Complexity Analysis image Compression Parallel Computing Neural Networks

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MODEL-BASED CODING OF FACIAL imageS BASED ON FACIAL MUSCLE MOTION THROUGH ISODENSITY MAPS

MODEL-BASED CODING OF FACIAL IMAGES BASED ON FACIAL MUSCLE M...

引用

CONF ON visual communications AND image processing : visual communications

作者： SO, I NAKAMURA, O MINAMI, T Kogakuin Univ. (Japan)

ISBN: (纸本)0819407429

A model-based coding system has come under serious consideration for the next generation of image coding schemes, aimed at greater efficiency in TV-telephone and TV-conference systems [l]-[5]. In this model-based coding system, the sender’s model image is transmitted and stored at the receiving side before the start of conversation. During the conversation, feature points are extracted from the facial image of the sender, and are transmitted to the receiver. The facial expression of the sender facial is reconstructed from the feature points received and a wireframe model constructed at the receiving side.

关键词： visual communications Model-based design Systems modeling Feature extraction image processing Motion models image compression Volume rendering visualization image visualization

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Collaborative image transmission over wireless sensor networks

Collaborative image transmission over wireless sensor networ...

引用

conference on visual communications and image processing 2004

作者： Wu, M Chen, CW Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

ISBN: (纸本)0819452114

The imaging sensors are able to provide intuitive visual information for quick recognition and decision. However, imaging sensors usually generate vast amount of data. Thus, processing of image data collected in the sensor network for the purpose of energy efficient transmission poses a significant technical challenge. In particular, when a cluster of imaging sensors is activated to track certain moving target, multiple sensors may be collecting similar visual information simultaneously. With correlated image data. we need to intelligently reduce the redundancy among the neighboring sensors so as to minimize the energy for transmission, the primary source of sensor energy consumption. We propose in this paper a novel collaborative image transmission scheme for wireless sensor networks. First, we apply a shape matching method to coarsely register images to find out maximal overlap in order to exploiting the spatial correlation between images acquired from neighboring sensors. A transformation is generated according to the matching results. We encode the original image and the difference between the transformed image and reference image. Then, we transmit the coded bit stream together with the transformation parameters. This will significantly reduce the transmission energy comparing with transmitting two individual images independently. To exploiting the temporal correlation among images in the same sensor, we assume that the imaging sensors and the background scenes remain stationary over the data acquisition period. For a given image sequence, we transmit back.-round image only once. A simple back-round subtraction method is employed to detect targets. Whenever targets are detected, only the regions of target and their spatial locations are transmitted to the monitoring center. At the monitoring center. the whole image can be reconstructed by fusing the background and the target image as well as its spatial location to further reduce energy consumption. Experimenta

关键词： collaborative image processing sensor network distribute image compression image registration backaround subtraction

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Coarse-to-fine Transformer For Lossless 3D Medical image Compression

Coarse-to-fine Transformer For Lossless 3D Medical Image Com...

引用

2024 conference on visual communications and image processing

作者： Yang, Xiaoxuan Lui, Guo Feng, Donghui Cheng, Zhengxue Yu, Guosheng Song, Li Shanghai Jiao Tong Univ Shanghai Peoples R China Alibaba Grp T Head Semicond Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798331529543;9798331529550

The rapid advancements in medical imaging have led to a growing demand for high-performance lossless compression of large 3D medical image datasets. Unlike natural images, medical images typically feature three-dimensional structures, and high bit-depth, necessitating specialized compression techniques. Based on a decoder-only transformer, we propose a learnable dual-decoder model for lossless compression of 3D medical images. Our approach packs voxels into patches, which are processed by a patch-level decoder to extract the patch feature. The voxels, along with the patch feature, are subsequently fed into a voxel-level decoder to model each voxel. This coarse-to-fine modeling strategy reduces the computational time for each voxel and enables long-range modeling dependencies. Experimental results demonstrate that our proposed model achieves state-of-the-art compression performance, with an approximately 15% improvement in compression performance over the traditional JP3D benchmark on various datasets.

关键词： Lossless image Compression Medical image Transformer

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See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recovery from a Single Rainy image

See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recove...

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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： Wang, Ping Wu, Wei Li, Zhu Liu, Yong Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO USA

ISBN: (纸本)9781728185514

Scale-Invariant Feature Transform (SIFT) is one of the most well-known image matching methods, which has been widely applied in various visual fields. Because of the adoption of a difference of Gaussian (DoG) pyramid and Gaussian gradient information for extrema detection and description, respectively, SIFT achieves accurate key points and thus has shown excellent matching results but except under adverse weather conditions like rain. To address the issue, in the paper we propose a divide-and-conquer SIFT key points recovery algorithm from a single rainy image. In the proposed algorithm, we do not aim to improve quality for a derained image, but divide the key point recovery problem from a rainy image into two sub-problems, one being how to recover the DoG pyramid for the derained image and the other being how to recover the gradients of derained Gaussian images at multiple scales. We also propose two separate deep learning networks with different losses and structures to recover them, respectively. This divide-and-conquer scheme to set different objectives for SIFT extrema detection and description leads to very robust performance. Experimental results show that our proposed algorithm achieves state-of-the-art performances on widely used image datasets in both quantitative and qualitative tests.

关键词： SIFT key point recovery rainy image extrema detection and description deep learning

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