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

作者： Wang, Caixia Song, Zehai Feng, Songhe Lang, Congyan Yan, Shuicheng Beijing Jiaotong Univ Beijing Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781479902880

As the explosive growth of the web image data, image tag ranking used for image retrieval accurately from mass images is becoming an active research topic. However, the existing ranking approaches are not very ideal, which remains to be improved. This paper proposed a new image tag saliency ranking algorithm based on sparse representation. we firstly propagate labels from image-level to region-level via Multi-instance Learning driven by sparse representation, which means reconstructing the target instance from positive bag via the sparse linear combination of all the instances from training set, instances with nonzero reconstruction coefficients are considered to be similar to the target instance;then visual attention model is used for tag saliency analysis. Comparing with the existing approaches, the proposed method achieves a better effect and shows a better performance.

关键词： tag saliency ranking sparse representation multi-instance learning Diverse Density visual attention model

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A Unified Single image De-raining Model via Region Adaptive Coupled Network

A Unified Single Image De-raining Model via Region Adaptive ...

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

作者： Wu, Qingbo Chen, Li Ngan, King Ngi Li, Hongliang Meng, Fanman Xu, Linfeng Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

ISBN: (纸本)9781728180687

Single image de-raining is quite challenging due to the diversity of rain types and inhomogeneous distributions of rainwater. By means of dedicated models and constraints, existing methods perform well for specific rain type. However, their generalization capability is highly limited as well. In this paper, we propose a unified de-raining model by selectively fusing the clean background of the input rain image and the well restored regions occluded by various rains. This is achieved by our region adaptive coupled network (RACN), whose two branches integrate the features of each other in different layers to jointly generate the spatial-variant weight and restored image respectively. On the one hand, the weight branch could lead the restoration branch to focus on the regions with higher contributions for de-raining. On the other hand, the restoration branch could guide the weight branch to keep off the regions with over-/under-filtering risks. Extensive experiments show that our method outperforms many state-of-the-art de-raining algorithms on diverse rain types including the rain streak, raindrop and rain-mist.

关键词： Single image de-raining region adaptive coupled network weighted fusion model

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Wearable visually Assistive Device for Blind People to Appreciate Real-world Scene and Screen image

Wearable Visually Assistive Device for Blind People to Appre...

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

作者： Ai, Jin Hu, Menghan Li, Guodong Zhai, Guangtao Zhang, Jian Li, Qingli Sun, Wendell Q. Univ Shanghai Sci & Technol Sch Med Instrument & Food Engn Shanghai Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Minist Educ Key Lab Articial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728180687

Due to the loss of vision, the appreciation of the real-world scene and the images displayed on the screen becomes almost impossible for blind people. In an effort to meet the needs of the blind community, we develop a wearable visually assistive device to help them perceive images. With the help of various multimedia information processing technologies, the proposed device can first acquire image information through a depth camera, then implement an image-to-text transformation using image caption technology, and finally the obtained text sequence is fed back to the user via voice. In this way, blind people are able to perceive the outside world, thus creating an unprecedented experience for them. The main technical specifications of the system are: distance perception range is 0.1m to 10m;RGB field of view is 69.4 degrees x42.5 degrees x77 degrees;depth field of view is 91.2 degrees x65.5 degrees x 100.6 degrees;maximum weight is 3.05kg. Two demo videos of the proposed navigation system which are respectively recorded for real- world scene and screen image are available at: https://***/10.6084/***.12520499. v1

关键词： blindness assistive system vision disorder

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Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization

Bit Distribution Study and Implementation of Spatial Quality...

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2024 conference on visual communications and image processing

作者： Jia, Panqi Mao, Jue Koyuncu, Esin Koyuncu, A. Burakhan Solovyev, Timofey Karabutov, Alexander Zhao, Yin Alshina, Elena Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Nurnberg Germany Tech Univ Munich Chair Media Technol Munich Germany Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these properties, leading to the standardization effort of JPEG-AI. The JPEG-AI verification model has been released and is currently under development for standardization. Utilizing neural networks, it can outperform the classic codec VVC intra by over 10% BD-rate operating at base operation point. Researchers attribute this success to the flexible bit distribution in the spatial domain, in contrast to VVC intra's anchor that is generated with a constant quality point. However, our study reveals that VVC intra displays a more adaptable bit distribution structure through the implementation of various block sizes. As a result of our observations, we have proposed a spatial bit allocation method to optimize the JPEG-AI verification model's bit distribution and enhance the visual quality. Furthermore, by applying the VVC bit distribution strategy, the objective performance of JPEG-AI verification mode can be further improved, resulting in a maximum gain of 0.45 dB in PSNR-Y.

关键词： Learned image Compression JPEG-AI bit rate matching algorithm optimization

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Comparison of the State-of-the-Art image and Video Quality Assessment Metrics 29

Comparison of the State-of-the-Art Image and Video Quality A...

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29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Akkaya, Emre Ozbek, Nukhet Ege Univ Muhendislik Fak Elekt Elekt Muhendisligi Bolumu Izmir Turkey

ISBN: (纸本)9781665436496

Today, thanks to the developing technology, changes are experienced in many areas and these changes sometimes bring some problems, issues that need to be solved or improved. In the field of digital image and video processing, the viewer experience has been greatly improved with the development of image and video coding methods and technologies in recent years. However, due to these developments, the importance of video and image quality evaluation techniques has increased. In the field of imaging technology, although the human visual system and human judgment experiments will give us the most accurate results, this rapidly growing field has made human judgment experiments unsuitable with the increasing content. At this point, effective and automatic quality assessment metrics are needed to evaluate and optimize advanced compression technologies in terms of visual perception quality. In this paper, our aim is to compare the performances of some of the new generation image and video quality assessment metrics.

关键词： human visual system traditional image&video quality metrics the state-of-the-art image and video quality metrics

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NATURAL imageS SCALE INVARIANCE AND HIGH-FIDELITY image RESTORATION

NATURAL IMAGES SCALE INVARIANCE AND HIGH-FIDELITY IMAGE REST...

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

作者： Guo, Hongwei Jiang, Feng Liu, Shaohui Zhao, Debin Harbin Inst Technol Sch Comp Harbin 150006 Peoples R China

ISBN: (纸本)9781479902880

One of the most striking properties of natural image statistics is their scale invariance. Intuitively, a natural image always contains the same contents of different scales and dually the same contents of same scale exist throughout scales of the image. Different from the previous scale invariance related work decomposing an image to its local band-pass filter components, this paper seeks a general model of the natural image paths distribution to describe the scale invariance in the visual world and then a novel strategy for high-fidelity image restoration is presented by characterizing nonlocal self-similarity of natural images throughout scales in a unified statistical manner, which offers a powerful mechanism of combining natural images scale invariance and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Extensive experiments on image restoration from partial random samples manifest that the proposed algorithm achieves significant performance improvements over the current state-of-the-art schemes.

关键词： scale invariance Gaussian Mixture Model image restoration similar patches

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Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization

Color Enhancement for V-PCC Compressed Point Cloud via 2D At...

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2024 conference on visual communications and image processing

作者： Bao, Jingwei Liu, Yu Li, Zeliang Zhu, Shuyuan Yeung, Siu-Kei Au Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Video-based point cloud compression (V-PCC) converts the dynamic point cloud data into video sequences using traditional video codecs for efficient encoding. However, this lossy compression scheme introduces artifacts that degrade the color attributes of the data. This paper introduces a framework designed to enhance the color quality in the V-PCC compressed point clouds. We propose the lightweight de-compression Unet (LDC-Unet), a 2D neural network, to optimize the projection maps generated during V-PCC encoding. The optimized 2D maps will then be back-projected to the 3D space to enhance the corresponding point cloud attributes. Additionally, we introduce a transfer learning strategy and develop a customized natural image dataset for the initial training. The model was then fine-tuned using the projection maps of the compressed point clouds. The whole strategy effectively addresses the scarcity of point cloud training data. Our experiments, conducted on the public 8i voxelized full bodies long sequences (8iVSLF) dataset, demonstrate the effectiveness of our proposed method in improving the color quality.

关键词： Point cloud compression image restoration Transfer learning Point cloud reconstruction

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image-Prompt Integration Network with Self-Ranking and Inter-Ranking Loss for AI-Generated image Quality Assessment

Image-Prompt Integration Network with Self-Ranking and Inter...

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2024 conference on visual communications and image processing

作者： Zhou, Tianwei Yao, Xizhang Tan, Songbai Ding, Xiaoying Yue, Guanghui Shenzhen Univ Coll Management Shenzhen Peoples R China Zhongnan Univ Econ & Law Sch Informat Engn Wuhan Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

AI-generated images (AGIs) are increasingly utilized across diverse domains due to their ability to quickly produce high-quality visuals. However, assessing the quality of AGIs remains challenging due to their inherent variability and distinctive distortions. To address these challenges, we propose a novel AGI quality assessment method named SIRQA, which enhances feature representation by integrating visual features with textual prompts, effectively measureing the alignment between the generated images and the described content to improve the precision of quality assessment. Specifically, SIRQA employs self-ranking and inter-ranking mechanisms to refine feature representation. The self-ranking mechanism maintains consistency between feature distances and sampling scales, making sure that features from similar sampling scales are positioned closer together. Additionally, inter-ranking mechanism sorts the weighted similarity scores between images and prompts to align with the ranking in the label space. Extensive experiments on the AGIQA3K and PKUI2IQA datasets show that our SIRQA outperforms eight state-of-the-art algorithms in terms of both Spearman's rank correlation coefficient (SRCC) and Pearson linear correlation coefficient (PLCC).

关键词： AI-generated images image quality assessment multi-scale feature image-prompt integration

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Robust image-processing filters preserving small features and rejecting impulse noise

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JOURNAL OF communications TECHNOLOGY AND ELECTRONICS 2001年第4期46卷 450-455页

作者： Kravchenko, VF Ponomarev, VI Pogrebnyak, AB Pustovoit, VI Niño-de-Rivera, L

New robust image-filtering algorithms based on RM point estimates and on the known KNN-filter technology are presented. These RM-KNN filters show a sufficiently high efficiency in rejecting pulse noise and preserving object boundaries and small features of the image. The filters were checked with test and real images typical of remote-sensing problems. The proposed filters provide a good visual quality of the filtered images and feature better characteristics than the standard median filter.

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Face detection and tracking using edge orientation information

Face detection and tracking using edge orientation informati...

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conference on visual communications and image processing 2001

作者： Fröba, B Küblbeck, C Fraunhofer Inst Integrated Circuits Dept Appl Elect D-91058 Erlangen Germany

ISBN: (纸本)0819439886

An important topic in face recognition as well as in video coding or multi-modal human machine interfaces is the automatic detection of faces or head-and-shoulder regions in visual scenes. The algorithms therefore should be computationally fast enough to allow an online detection and parallel processing of the detected objects. In this paper we describe our ongoing work on face detection using an approach that models the face appearance by edge orientation information. We will show that edge orientation is a powerful local image feature to describe objects like faces or body parts. We will present a simple and efficient method for template matching and object modeling based solely on edge orientation information. We also show how to obtain an optimal face model in the edge orientation domain from a set of training images. Unlike many approaches that model the grey level appearance of the face our approach is computationally very fast. It takes less than 0.1 seconds on a Pentium II 500MHz for a 190x140 image to be processed using a multi-resolution search with three resolution levels. We demonstrate the capability of our detection method on an image database of 7000 images taken from more than 200 different people. The variations in head size, lighting and background are considerable. The obtained detection rate is more than 97% on that database. We hare also extended the algorithm for face tracking. The tracking capabilities are shown using results from a real - time implementation of the proposed algorithm.

关键词： face detection edge orientation image segmentation object recognition face tracking

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