2023 Data Compression Conference (DCC)最新文献_第8页

Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction 使用跨尺度、跨组和跨颜色预测的无损点云属性压缩

2023 Data Compression Conference (DCC) Pub Date : 2023-03-01 DOI: 10.1109/DCC55655.2023.00031

Jianqiang Wang, Dandan Ding, Zhan Ma

{"title":"Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction","authors":"Jianqiang Wang, Dandan Ding, Zhan Ma","doi":"10.1109/DCC55655.2023.00031","DOIUrl":"https://doi.org/10.1109/DCC55655.2023.00031","url":null,"abstract":"This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency. Specifically, we first generate multiscale attribute tensors through average pooling, by which, for any two consecutive scales, the decoded lower-scale attribute can be used to estimate the attribute probability in the current scale in one shot. Additionally, in each scale, we perform the probability estimation group-wisely following a predefined grouping pattern. In this way, both cross-scale and (same-scale) cross-group correlations are exploited jointly. Furthermore, cross-color redundancy is removed by allowing inter-color processing for YCoCg/RGB alike multi-channel attributes. The proposed method not only demonstrates state-of-the-art compression efficiency with significant performance gains over the latest G-PCC on various contents but also sustains low complexity with affordable encoding and decoding runtime.","PeriodicalId":209029,"journal":{"name":"2023 Data Compression Conference (DCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126848188","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}

引用次数: 0

RNA secondary structures: from ab initio prediction to better compression, and back RNA二级结构:从从头开始预测到更好的压缩，再回来

2023 Data Compression Conference (DCC) Pub Date : 2023-02-22 DOI: 10.1109/DCC55655.2023.00036

Eva Onokpasa, Sebastian Wild, Prudence W. H. Wong

引用次数: 0

Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis 学习压缩无人机捕获的视频:基准和分析

2023 Data Compression Conference (DCC) Pub Date : 2023-01-15 DOI: 10.48550/arXiv.2301.06115

Chuanmin Jia, Feng Ye, Huifang Sun, Siwei Ma, Wen Gao

{"title":"Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis","authors":"Chuanmin Jia, Feng Ye, Huifang Sun, Siwei Ma, Wen Gao","doi":"10.48550/arXiv.2301.06115","DOIUrl":"https://doi.org/10.48550/arXiv.2301.06115","url":null,"abstract":"In this paper, we propose to build a novel benchmark and neural video coding task named learning based Unmanned Aerial Vehicle (UAV) video coding. We collect the UAV videos with different content variations, including in-door and out-door scenes, object-scale variations and viewpoint distance, different climate condition etc. Then we encode those properly-selected videos using popular end-to-end optimized video codecs and conventional hybrid codecs, to form a comprehensive benchmark for learned drone video compression. We also provide a detailed analysis and envision the challenge of such task for future research. The main contributions of this paper are three folds. First, we construct a comprehensive benchmark for the task of drone video compression which consists of the rate-distortion (R-D) behavior of both hybrid and learned video codecs. To our knowledge, it is the first attempt in end-to-end optimized solution to compress drone videos. Second, we provide the review and analysis of the learned drone video compression schemes and further discuss the challenges of encoding UAV videos. Third, this benchmark and related research is accomplished as a milestone MPAI End-to-end Video (EEV) coding project. The proposed benchmark has constructed a solid baseline for compressing UAV videos and facilitates the future research works for related task.","PeriodicalId":209029,"journal":{"name":"2023 Data Compression Conference (DCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133278083","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}

引用次数: 2

Computing matching statistics on Wheeler DFAs 计算惠勒dfa的匹配统计

2023 Data Compression Conference (DCC) Pub Date : 2023-01-13 DOI: 10.1109/DCC55655.2023.00023

A. Conte, Nicola Cotumaccio, T. Gagie, G. Manzini, N. Prezza, M. Sciortino

引用次数: 5

Linear Computation Coding: Exponential Search and Reduced-State Algorithms 线性计算编码:指数搜索和状态简化算法

2023 Data Compression Conference (DCC) Pub Date : 2023-01-13 DOI: 10.1109/DCC55655.2023.00038

Hans Rosenberger, Johanna S. Fröhlich, Ali Bereyhi, R. Müller

引用次数: 1

Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines 用于人类和机器的可扩展图像编码的学习解纠缠潜在表示

2023 Data Compression Conference (DCC) Pub Date : 2023-01-10 DOI: 10.1109/DCC55655.2023.00012

Ezgi Ozyilkan, Mateen Ulhaq, Hyomin Choi, Fabien Racapé

引用次数: 3

Adaptive and Scalable Compression of Multispectral Images using VVC 使用VVC的多光谱图像的自适应和可伸缩压缩

2023 Data Compression Conference (DCC) Pub Date : 2023-01-10 DOI: 10.1109/DCC55655.2023.00062

Philipp Seltsam, P. Das, M. Wien

{"title":"Adaptive and Scalable Compression of Multispectral Images using VVC","authors":"Philipp Seltsam, P. Das, M. Wien","doi":"10.1109/DCC55655.2023.00062","DOIUrl":"https://doi.org/10.1109/DCC55655.2023.00062","url":null,"abstract":"The VVC codec is applied to the task of multispectral image (MSI) compression using adaptive and scalable coding structures. In a “plain” VVC approach, concepts from picture-to-picture temporal prediction are employed for decorrelation along the MSI’s spectral dimension. The popular principle component analysis (PCA) for spectral decorrelation is further evaluated in combination with VVC intra-coding for spatial decorrelation. This approach is referred to as PCA-VVC. A novel adaptive MSI compression algorithm, named HPCLS, is introduced, that uses PCA and inter-prediction for spectral and VVC intra-coding for spatial decorrelation. Further, a novel adaptive scalable approach is proposed, that provides a separately decodable spectrally scaled preview of the MSI in the compressed file. Information contained in the preview is exploited in order to reduce the overall file size. All schemes are evaluated on images from the ARAD HS data set containing outdoor scenes with a high variety in brightness and color. We found that “Plain” VVC is outperformed by both PCA-VVC and HPCLS. HPCLS shows advantageous rate-distortion (RD) behavior compared to PCA-VVC for reconstruction quality above 51 dB PSNR. The performance of the scalable approach is compared to the combination of an independent RGB preview and one of HPCLS or PCA-VVC denoted as simulcast. The scalable approach shows significant benefit especially at higher preview qualities. A more detailed version of this article can be found on arXiv1.","PeriodicalId":209029,"journal":{"name":"2023 Data Compression Conference (DCC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116615272","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}

引用次数: 1

Abstract Huffman Coding and PIFO Tree Embeddings Huffman编码和PIFO树嵌入

2023 Data Compression Conference (DCC) Pub Date : 2023-01-07 DOI: 10.1109/DCC55655.2023.00077

Keri D’Angelo, D. Kozen

引用次数: 1

Applicability limitations of differentiable full-reference image-quality metrics 可微全参考图像质量度量的适用性限制

2023 Data Compression Conference (DCC) Pub Date : 2022-12-11 DOI: 10.20948/prepr-2022-86

Maksim Siniukov, D. Kulikov, D. Vatolin

引用次数: 1

Computing the optimal BWT of very large string collections 计算非常大的字符串集合的最佳BWT

2023 Data Compression Conference (DCC) Pub Date : 2022-12-02 DOI: 10.1109/DCC55655.2023.00015

David Cenzato, V. Guerrini, Zsuzsanna Lipt'ak, Giovanna Rosone

{"title":"Computing the optimal BWT of very large string collections","authors":"David Cenzato, V. Guerrini, Zsuzsanna Lipt'ak, Giovanna Rosone","doi":"10.1109/DCC55655.2023.00015","DOIUrl":"https://doi.org/10.1109/DCC55655.2023.00015","url":null,"abstract":"It is known that the exact form of the Burrows-Wheeler Transform (BWT) of a string collection depends, in most implementations, on the input order of the strings in the collection. Reordering strings of an input collection affects the number of equal-letter runs r, arguably the most important parameter of BWT-based data structures, such as the FM-index or the r-index. Bentley, Gibney, and Thankachan [ESA 2020] introduced a linear-time algorithm for computing the permutation of the input collection which yields the minimum number of runs of the resulting BWT. In this paper, we present the first tool that guarantees a Burrows-Wheeler Transform with minimum number of runs (optBWT), by combining i) an algorithm that builds the BWT from a string collection (either SAIS-based [Boucher et al., SPIRE 2021] or BCR [Bauer et al., CPM 2011]); ii) the SAP array data structure introduced in [Cox et al., Bioinformatics, 2012]; and iii) the algorithm by Bentley et al. We present results both on real-life and simulated data, showing that the improvement achieved in terms of r with respect to the input order is significant and the overhead created by the computation of the optimal BWT negligible, making our tool competitive with other tools for BWT-computation in terms of running time and space usage. In particular, on real data the optBWT obtains up to 31 times fewer runs with only a 1.39$times$ slowdown. Source code is available at https://github.com/davidecenzato/optimalBWT.git.","PeriodicalId":209029,"journal":{"name":"2023 Data Compression Conference (DCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338758","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}

引用次数: 3