2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)最新文献

{"title":"Spike Signal Reconstruction Based on Inter-Spike Similarity","authors":"Yiyang Zhang, Ruiqin Xiong, Tiejun Huang","doi":"10.1109/VCIP56404.2022.10008868","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008868","url":null,"abstract":"Spike camera is a kind of bio-inspired camera which is particularly proposed for capturing dynamic scenes with high speed motion. Spike camera works in a way simulating the retina that it receives incoming photons continuously and fires a spike whenever the accumulated photons reach a threshold. The spike stream can be recorded at an extremely high temporal resolution so that the dynamic process of light-intensity changes may be recovered. This paper addresses the problem of recovering the original visual signal from spikes. To reduce the fluctuation in spike intervals caused by the Poisson effect of photon arrivals and the quantization effect in spike reading, we estimate the real interval from a sequence of temporally neighboring spikes. To avoid mixing the spikes generated from significantly different light intensities, we propose a temporal and spatial weighting method based on the inter-spike similarity. Experimental results demonstrate that the proposed method outperforms the previous light intensity inference methods and achieves better performance under different motion conditions.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized MobileNetV2 Based on Model Pruning for Image Classification","authors":"Peng Xiao, Yuliang Pang, Hao Feng, Yu Hao","doi":"10.1109/VCIP56404.2022.10008829","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008829","url":null,"abstract":"Due to the large memory requirement and a large amount of computation, traditional deep learning networks cannot run on mobile devices as well as embedded devices. In this paper, we propose a new mobile architecture combining MobileNetV2 and pruning, which further decreases the Flops and number of parameters. The performance of MobileNetV2 has been widely demonstrated, and pruning operation can not only allow further model compression but also prevent overfitting. We have done ablation experiments at CIIP Tire Data for different pruning combinations. In addition, we introduced a global hyperparameter to effectively weigh the accuracy and precision. Experiments show that the accuracy of 98.3 % is maintained under the premise that the model size is only 804.5 KB, showing better performance than the baseline method.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126820057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinguishing Computer-Generated Images from Photographic Images: a Texture-Aware Deep Learning-Based Method","authors":"Zicheng Zhang, Wei Sun, Xiongkuo Min, Tao Wang, Wei Lu, Guangtao Zhai","doi":"10.1109/VCIP56404.2022.10008854","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008854","url":null,"abstract":"With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133165616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-stage Locally and Long-range Correlated Feature Fusion for Learned In-loop Filter in VVC","authors":"B. Kathariya, Zhu Li, Hongtao Wang, G. V. D. Auwera","doi":"10.1109/VCIP56404.2022.10008834","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008834","url":null,"abstract":"Versatile Video Coding (VVC)/H.266 is currently the state-of-the-art video coding standard with significant improvement in coding efficiency over its predecessor High Efficiency Video Coding (HEVC)/H.26S. Nonetheless, VVC is also block-based video coding technology where decoded pictures contain compression artifacts. In VVC, in-loop filters serve to suppress these compression artifacts. In this paper, convolution neural network (CNN) is utilized to better facilitate the suppression of compression artifacts over VVC. Nonetheless, our approach has uniqueness in obtaining better features by exploiting locally correlated spatial features in the pixel domain as well as long-range correlated spectral features in the discrete cosine transform (DCT) domain. In particular, we utilized CNN-features from DCT transformed input to extract high-frequency components and induce long-range correlation into the spatial CNN-features by employing multi-stage feature fusion. Our experimental result shows that the proposed approach achieves significant coding improvements up to 9.70% on average Bjantegaard Delta (BD)-Bitrate savings under AI configurations for luma (Y) components.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133340255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic Attribute Guided Image Aesthetics Assessment","authors":"Jiachen Duan, Pengfei Chen, Leida Li, Jinjian Wu, Guangming Shi","doi":"10.1109/VCIP56404.2022.10008896","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008896","url":null,"abstract":"Image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, object emphasis, etc. However, the state-of-the-art IAA approaches usually follow the data-driven framework without considering the rich attributes contained in images. With this motivation, this paper presents a new semantic attribute guided IAA model, where the attention maps of semantic attributes are employed to enhance the representation ability of general aesthetic features for more effective aesthetics assessment. Specifically, we first design an attribute attention generation network to obtain the attention maps for different semantic attributes, which are utilized to weight the general aesthetic features, producing the semantic attribute-enhanced feature representations. Then, the Graph Convolutional Network (GCN) is employed to further investigate the inherent relationship among the enhanced aesthetic features, producing the final image aesthetics prediction. Extensive experiments and comparisons on three public IAA databases demonstrate the effectiveness of the proposed method.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Sparsity Analysis of Light Field Signal For Capturing Optimization of Multi-view Images","authors":"Ying Wei, Changjian Zhu, Qiuming Liu","doi":"10.1109/VCIP56404.2022.10008843","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008843","url":null,"abstract":"In the previous results, light field sampling is based on ideal assumptions (e.g., Lambertian and Non-occluded scene), and thus we would like to more precisely analyze the sparsity sampling of light field signal. We present a sparsity analysis of light field (SALF) method for optimizing light field sampling rate. The SALF method applies the Fourier projection-slice theorem to simplify the initialization of light field sampling. Furthermore, we use a voting scheme to select light field spectra in which the frequency coefficients are nonzero. These spectra include many scene information and their captured positions are approximately equal to camera positions in the frequency domain. If the camera is only placed in these selected camera positions, the sampling rate can be optimized and the rendering quality can be guaranteed. Finally, we compare SALF method with other light field sampling methods to verify the claimed performance. The reconstruction results show that the SALF method improves rendering quality of novel views and outperforms those of other comparison methods.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132629505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Interpolation Filters for Chroma Motion Compensation in Video Coding","authors":"Xinsheng Xie, Kai Zhang, Li Zhang, Meng Wang, Junru Li, Shiqi Wang","doi":"10.1109/VCIP56404.2022.10008810","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008810","url":null,"abstract":"Interpolation filters for motion compensation play an important role in video coding. Until recently, interpolation filters for luma motion compensation (MC) draw most of the attention while interpolation filters for chroma motion compen-sation are less noticeable. This paper comprehensively analyzes different types of interpolation filters and presents 6-tap DCT based chroma interpolation filters for chroma MC. The proposed interpolation filters are designed considering multiple factors, comprising the characteristics of the chroma component, the frequency response of interpolation filter and the expenses of computational complexity. The experimental results show the proposed method can achieve 0.05%, 1.23% and 1.25% BD-rate reductions on average for Y, Cb and Cr components, respectively, with only 2% decoding time increasing under Random Access configuration, compared with ECM-4.0, which is a reference software developed by JVET, targeting at future video coding technologies beyond VVC. Owing to the good trade-off between coding performance and complexity, the proposed method has been adopted in ECM.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114697356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Pre-chewing Compression Degradation for Learned Video Compression","authors":"Man M. Ho, Heming Sun, Zhiqiang Zhang, Jinjia Zhou","doi":"10.1109/VCIP56404.2022.10008873","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008873","url":null,"abstract":"Artificial Intelligence (AI) needs huge amounts of data, and so does Learned Restoration for Video Compression. There are two main problems regarding training data. 1) Preparing training compression degradation using a video codec (e.g., Versatile Video Coding - VVC) costs a considerable resource. Significantly, the more Quantization Parameters (QPs) we compress with, the more coding time and storage are required. 2) The common way of training a newly initialized Restoration Network on pure compression degradation at the beginning is not effective. To solve these problems, we propose a Degradation Network to pre-chew (generalize and learn to synthesize) the real compression degradation, then present a hybrid training scheme that allows a Restoration Network to be trained on unlimited videos without compression. Concretely, we propose a QP-wise Degradation Network to learn how to compress video frames like VVC in real-time and can transform the degradation output between QPs linearly. The real compression degradation is thus pre-chewed as our Degradation Network can synthesize the more generalized degradation for a newly initialized Restoration Network to learn easier. To diversify training video content without compression and avoid overfitting, we design a Training Framework for Semi-Compression Degradation (TF-SCD) to train our model on many fake compressed videos together with real compressed videos. As a result, the Restoration Network can quickly jump to the near-best optimum at the beginning of training, proving our promising scheme of using pre-chewed data for the very first steps of training. In other words, a newly initialized Learned Video Compression can be warmed up efficiently but effectively with our pre-trained Degradation Network. Besides, our proposed TF-SCD can further enhance the restoration performance in a specific range of QPs and provide a better generalization about QPs compared with the common way of training a restoration model. Our work is available at https://minhmanho.github.io/prechewing_degradation.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115438550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ERINet: Effective Rotation Invariant Network for Point Cloud based Place Recognition","authors":"Shichen Weng, Ruonan Zhang, Ge Li","doi":"10.1109/VCIP56404.2022.10008830","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008830","url":null,"abstract":"Place recognition task is a crucial part of 3D scene recognition in various applications. Nowadays, learning-based point cloud place recognition approaches have achieved remarkable success. However, these methods seldom consider the possible rotation of point cloud data in large-scale real-world place recognition tasks. To cope with this problem, in this work, we propose a novel effective rotation invariant network for large-scale place recognition named ERINet, which captures the recent successful deep network architecture and benefits from holding the rotation-invariant property of point clouds. In this network, we design a core effective rotation invariant module, which enhances the ability to extract rotation-invariant features of 3D point clouds. The benchmark experiments illustrate that our network boosts the performance of the recent works on all evaluation metrics with various rotations, even under challenging rotation cases.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115622212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry Reconstruction for Spatial Scalability in Point Cloud Compression Based on the Prediction of Neighbours' Weights","authors":"Zhang Chen, Shuai Wan","doi":"10.1109/VCIP56404.2022.10008805","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008805","url":null,"abstract":"Spatial scalability is a critical feature in geometrybased point cloud compression (G-PCC). The current design of geometry reconstructions for spatial scalability applies points in fixed positions (center of nodes) and ignores the connection of points in regions. This work analyses the correlation between neighbours' occupancy and locally optimal reconstruction points within a node using the Pearson Product Moment Correlation Coefficient (PPMCC). Then we propose a geometry reconstruction method based on predicting the neighbours' weights. Geometry reconstruction points are calculated by applying weights inverse to distance to different categories of neighbours (face neighbours, edge neighbours, corner neighbours). Compared to the state-of-the-art G-PCC, performance improvement of 1.03dB in D1-PSNR and 2.90dB in D2-PSNR, on average, can be observed using the proposed method. Meanwhile, a simplified method is available to satisfy different complexity requirements.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124220250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}