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DCC 2027: Data Compression Conference

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截稿日期:
2026-10-02
通知日期:
2026-11-22
会议日期:
2027-03-23
会议地点:
Snowbird, Utah, USA
届数:
加入日历: Outlook / Apple 日历（.ics）
CCF: B   ICORE: B   QUALIS: A2   浏览: 79984   关注: 78   参加: 18
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会伴指数 (CP-I)

61.1 / 100
全站第 581 名 / 共 5,694 个会议 · 前 11%

理论与算法 第 38 / 142

学术认可 (35%)
92
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
19
社区关注 (10%)
69
资料公开度 (15%)
55

用到的输入: 收录等级：CCF B, ICORE B, QUALIS A2 · 有据可查的届次：1 · 在会伴关注它的研究者：78 人 · 过去 24 个月打开过本页的研究者：34 人

公开资料里还缺: 历年录用率 (+4.5) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-09

征稿

DCC 2027 (Data Compression Conference) is a CCF B / ICORE B / QUALIS A2 conference held in Snowbird, Utah, USA on 2027-03-23. The paper submission deadline is 2026-10-02. Acceptance notifications are sent on 2026-11-22.

The Data Compression Conference (DCC) is an international forum for current work on data compression and related applications. The conference addresses: Compression of specific types of data (text, images, video, etc.) Compression in networking, communications, and storage Applications to bioinformatics Applications to mobile computing Applications to information retrieval Computational issues for compression related applications Inpainting-based compression, perceptual coding Compressed data structures Quantization theory and vector quantization (VQ) Joint source-channel coding Compression-related standards Both theoretical and experimental work are of interest. Theme Topics of interest include but are not limited to: Lossless and lossy compression for storage and transmission of specific types of data (including text, gray scale and color photographs, multi-spectral and hyper-spectral images, palette images, video, movies, audio, music, maps, instrument and sensor data, space data, earth observation data, scientific data, weather data, medical data, graphics data, geometry data, 3D representations, animation, bi-level images / bit-maps, web content, web graphs, etc.); source coding; source coding in multiple-access networks; joint source-channel coding; rate-distortion coding; rate allocation; multiple-description coding; quantization theory; vector quantization (VQ); multiple description VQ; transform-based methods (including DCT and wavelet transforms); parallel compression algorithms and hardware; error-resilient compression techniques; adaptive compression algorithms; browsing and searching compressed data; compressed data structures; applications to immersive media; inpainting-based compression; perceptual coding; visual search; object recognition; applications of neural networks and deep learning (e.g., CNNs) to compression; string searching and manipulation used in compression applications; fractal-based compression methods; information retrieval employing compression techniques; steganography / hidden information with respect to compressed data; minimal-length encoding and applications to learning; system issues relating to data compression (including error control, data security, indexing, and browsing); compression applications and issues for computational biology and bioinformatics; compression applications and issues for the internet; compression applications and issues for mobile computing; applications of compression to file distribution and software updates; applications of compression to file storage and backup systems; applications of compression to data mining; applications of compression to image retrieval; applications of compression and information theory to human-computer interaction (HCI); development of and extensions to compression standards (including the HEVC, JPEG, MPEG, H.xxx, and G.xxx families and including compression of specific image types such as plenoptic images, point cloud images, and light field images); compressed sensing / compressive sampling; and the use of techniques from information theory and data compression in networking, communications, and storage of large data sets.
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