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

共 4342 条记录，以下是221-230 订阅

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Efficient Bitrate Ladder Construction for Per-Shot Adaptive Encoding

Efficient Bitrate Ladder Construction for Per-Shot Adaptive ...

引用

2024 conference on visual communications and image processing

作者： Zhao, Yan Cheng, ZhengXue Lu, Guo Xie, Rong Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

HTTP adaptive streaming (HAS) constructs bitrate ladders to deliver videos with the best possible quality under varying network conditions. Though per-shot content adaptive encoding (CAE) largely improves the compression efficiency by constructing the optimal bitrate ladder for each video shot, it suffers from excessive encoding complexity as all the points in the operating space (typically resolution x bitrate) need to be encoded and compared. To address this issue, this paper proposes an efficient bitrate ladder construction method that encodes only a subset of operating points, then uses curve fitting and inter-curve prediction to estimate other points' RD performance. The proposed method enables low-complexity ladder construction even for high-dimension operating spaces that incorporate dimensions like encoding presets. Experiments show that this method can achieve RD performance comparable to the original per-shot CAE with only 42% encoding points. Even when minimizing the encoding points to 3.6% of the original CAE, it achieves 15% BDRate improvements compared to using the fixed bitrate ladder.

关键词： Content-adaptive encoding per-shot encoding bitrate ladder prediction adaptive streaming

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Lightweight Color image Demosaicking with Multi-Core Feature Extraction

Lightweight Color Image Demosaicking with Multi-Core Feature...

引用

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

作者： Tan, Yufei Chang, Kan Li, Hengxin Tang, Zhenhua Qin, Tuanfa Guangxi Univ Sch Comp & Elect Informat Nanning Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techn Nanning Peoples R China

ISBN: (纸本)9781728180687

Convolutional neural network (CNN)-based color image demosaicking methods have achieved great success recently. However, in many applications where the computation resource is highly limited, it is not practical to deploy large-scale networks. This paper proposes a lightweight CNN for color image demosaicking. Firstly, to effectively extract shallow features, a multi-core feature extraction module, which takes the Bayer sampling positions into consideration, is proposed. Secondly, by taking advantage of inter-channel correlation, an attention-aware fusion module is presented to efficiently r econstruct t he full color image. Moreover, a feature enhancement module, which contains several cascading attention-aware enhancement blocks, is designed to further refine t he i nitial r econstructed i mage. To demonstrate the effectiveness of the proposed network, several state-of-the-art demosaicking methods are compared. Experimental results show that with the smallest number of parameters, the proposed network outperforms the other compared methods in terms of both objective and subjective qualities.

关键词： convolutional neural network image demosaicking feature extraction channel attention residual learning

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Motion detection in color image sequence and shadow elimination

Motion detection in color image sequence and shadow eliminat...

引用

conference on visual communications and image processing 2004

作者： Shen, J Univ Bordeaux 3 Inst EGID Bordeaux 3 F-33607 Pessac France

ISBN: (纸本)0819452114

Most of the researches are concentrated on motion detection in gray value image sequences and the methods for motion detection are based on background subtraction or on temporal gray value derivatives. The methods based on background subtraction, including auto-adaptive ones, meet difficulties in presence of illumination changes and of slowly moving objects and need to be re-initialized from time to time. The methods based on temporal derivatives are in general sensible to noise. Color images containing much richer information than the gray value ones, it would be interesting to use them to better detect moving objects. In this paper, we address the problem of motion detection in color image sequences and the problems of illumination changes and shadow elimination. Our motion detection method is based on fuzzy segmentation of the color difference image in help of non-symmetrical pi membership functions. The elimination of false moving objects detected due to illumination change is realized by combining the background subtraction method with the temporal derivative method and motion continuity. Shadows are removed by comparing the color of mobile pixels detected in the current frame with that in the precedent frame in HSL color space. Experimental results are reported.

关键词： color image sequence motion detection fuzzy deduction illumination change shadow elimination

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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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Complex Event Recognition via Spatial-Temporal Relation Graph Reasoning

Complex Event Recognition via Spatial-Temporal Relation Grap...

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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： Lin, Huan Zhao, Hongtian Yang, Hua Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781728185514

Events in videos usually contain a variety of factors: objects, environments, actions, and their interaction relations, and these factors as the mid-level semantics can bridge the gap between the event categories and the video clips. In this paper, we present a novel video events recognition method that uses the graph convolution networks to represent and reason the logic relations among the inner factors. Considering that different. kinds of events may focus on different factors, we especially use the transformer networks to extract the spatial-temporal features drawing upon the attention mechanism that can adaptively assign weights to concerned key factors. Although transformers generally rely more on large datasets, we show the effectiveness of applying a 2D convolution backbone before the transformers. We train and test our framework on the challenging video event recognition dataset UCF-Crime and conduct ablation studies. The experimental results show that our method achieves state-of-the-art performance, outperforming previous principal advanced models with a significant margin of recognition accuracy.

关键词： Video understanding Relation graph Transformer Graph convolution network

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HDR image Compression with Convolutional Autoencoder

HDR Image Compression with Convolutional Autoencoder

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

作者： Han, Fei Wang, Jin Xiong, Ruiqin Zhu, Qing Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China

ISBN: (纸本)9781728180687

As one of the next- generation multimedia technology, high dynamic range (HDR) imaging technology has been widely applied. Due to its wider color range, HDR image brings greater compression and storage burden compared with traditional LDR image. To solve this problem, in this paper, a two-layer HDR image compression framework based on convolutional neural networks is proposed. The framework is composed of a base layer which provides backward compatibility with the standard JPEG, and an extension layer based on a convolutional variational autoencoder neural networks and a post-processing module. The autoencoder mainly includes a nonlinear transform encoder, a binarized quantizer and a nonlinear transform decoder. Compared with traditional codecs, the proposed CNN autoencoder is more flexible and can retain more image semantic information, which will improve the quality of decoded HDR image. Moreover, to reduce the compression artifacts and noise of reconstructed HDR image, a post-processing method based on group convolutional neural networks is designed. Experimental results show that our method outperforms JPEG XT profile A, B, C and other methods in terms of HDR-VDP-2 evaluation metric. Meanwhile, our scheme also provides backward compatibility with the standard JPEG.

关键词： Convolutional neural networks

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image enhancement circuit using non-linear processing curve and constrained histogram range equalization

Image enhancement circuit using non-linear processing curve ...

引用

conference on visual communications and image processing 2004

作者： Cvetkovic, SD de With, PHN Bosch Secur Syst NL-5616 LW Eindhoven Netherlands

ISBN: (纸本)0819452114

For real-time imaging in surveillance applications. image fidelity is of primary importance to ensure customer confidence. The obtained image fidelity is a result from amongst others dynamic range expansion and video signal enhancement. The dynamic range of the signal needs adaptation. because the sensor signal has a much larger range than the standard CRT display. The signal enhancement should accommodate for the widely varying light and scene conditions and user scenarios of the equipment. This paper proposes a new system to combine dynamic range and enhancement processing, offering a strongly improved picture quality for surveillance applications. The key to Our Solution is that we use Non-Linear processing (NLP) with a so-called Constrained Histogram Range Equalization (CHRE). The NLP transforms the digitized high-dynarnic lurninance sensor Signal such that details of' the low-luminance parts arc enhanced, while avoiding detail losses in the high-luminance areas. The CHRE technique enhances visibility of the global contrast for the camera signal Without significant information loss in the statistically less relevant areas. Evaluations of this proposal have shown clear improvements of the perceptual image quality. An additional advantage is that the new scheme is adaptable and allows the concatenation of further enhancement techniques without sacrificing the obtained picture quality improvement.

关键词： video signal enhancement dynamic range expansion contrast enhancement histogram equalization

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Robust traffic event extraction from surveillance video

Robust traffic event extraction from surveillance video

引用

conference on visual communications and image processing 2004

作者： Yoneyama, A Yeh, CH Kuo, CCJ Kokusai Denshin Denwa Co Ltd Res & Dev Labs Multimedia Commun Lab Kamifukuoka Saitama 3568502 Japan

ISBN: (纸本)0819452114

An approach to extract traffic events by integrating the low-level, middle-level, and high-level feature extraction modules is developed in this research. To be more specific, the low-level module extracts features such as motion, size, and location. The middle-level module builds a bridge between the road surface plane in the real world and the captured image plane by geometric analysis. Finally, the high-level module looks for traffic events such as "traffic jam", "lane change", and "traffic rule violation", which require the understanding of the video contents in a specific knowledge domain. In the high-level module, various traffic events are related to motion characteristics obtained from the middle-level module. It is demonstrated by experimental results that the proposed system can achieve robust traffic event extraction. The effectiveness of the proposed technique is analyzed. Conventional traffic event extraction methods demand the knowledge of capturing conditions for camera calibration. This requirement can be greatly relaxed in our proposed scheme.

关键词： traffic monitoring image analysis projection object tracking traffic events

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On Data Annotation Efficiency for image Based Crowd Counting

On Data Annotation Efficiency for Image Based Crowd Counting

引用

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

作者： Ma, Tianfang Liu, Shuoyan Wang, Qian Tianjin Univ Tianjin Peoples R China Natl Univ Singapore Singapore Singapore Univ Durham Durham England

ISBN: (纸本)9781665475921

Crowd counting aims at automatically estimating the number of persons in still images. It has attracted much attention due to its potential usage in surveillance, intelligent transportation and many other scenarios. In the recent decade, most researchers have been focusing on the design of novel deep learning models for improved crowd counting performance. Such attempts include proposing advanced architectures of deep neural networks, using different training strategies and loss functions. Other than the capabilities of models, the crowd counting performance is also determined by the quantity and the quality of training data. Whilst the deep models are data-hungry and better performance can usually be expected with more training data, annotating images for training is time-consuming and expensive in real-world applications. In this work, we focus on the efficiency of data annotation for crowd counting. By varying the number of annotated images and the number of annotated points (one point is annotated per person head) for training, our experimental results demonstrate it is more efficient to annotate a small number of points per image across a large number of images for training. Based on this conclusion, we present a novel adaptive scaling mechanism for data augmentation to diversify the training images without extra annotation cost. The mechanism is proved effective via thorough experiments.

关键词： Crowd counting Data annotation efficiency Data augmentation Adaptive scaling

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Tell Codec What Worth Compressing: Semantically Disentangled image Coding for Machine with LMMs

Tell Codec What Worth Compressing: Semantically Disentangled...

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2024 conference on visual communications and image processing

作者： Liu, Jinming Wei, Yuntao Lin, Junyan Zhao, Shengyang Sun, Heming Chen, Zhibo Zeng, Wenjun Jin, Xin Shanghai Jiao Tong Univ Shanghai Peoples R China Ningbo Inst Digital Twin Eastern Inst Technol Ningbo Peoples R China Univ Sci & Technol China Hefei Peoples R China Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9798331529543;9798331529550

We present a new image compression paradigm to achieve "intelligently coding for machine" by cleverly leveraging the common sense of Large Multimodal Models (LMMs). We are motivated by the evidence that large language/multimodal models are powerful general-purpose semantics predictors for understanding the real world. Different from traditional image compression typically optimized for human eyes, the image coding for machines (ICM) framework we focus on requires the compressed bitstream to more comply with different downstream intelligent analysis tasks. To this end, we employ LMM to tell codec what to compress: 1) first utilize the powerful semantic understanding capability of LMMs w.r.t object grounding, identification, and importance ranking via prompts, to disentangle image content before compression, 2) and then based on these semantic priors we accordingly encode and transmit objects of the image in order with a structured bitstream. In this way, diverse vision benchmarks including image classification, object detection, instance segmentation, etc., can be well supported with such a semantically structured bitstream. We dub our method "SDComp" for "Semantically Disentangled Compression", and compare it with state-of-the-art codecs on a wide variety of different vision tasks. SDComp codec leads to more flexible reconstruction results, promised decoded visual quality, and a more generic/satisfactory intelligent task-supporting ability.

关键词： image Coding for Machine Large Multimodal Model Semantically Structured Bitstream

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