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DCC 2027: Data Compression Conference

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投稿締切日:
2026-10-02
通知日:
2026-11-22
開催日:
2027-03-23
開催地:
Snowbird, Utah, USA
開催回数:
カレンダーに追加: Outlook / Apple（.ics）
CCF: B   ICORE: B   QUALIS: A2   閲覧: 79983   フォロー: 78   参加: 18
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DCC
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会伴インデックス (CP-I)

61.1 / 100
全 5,694 件中 第 581 位 · 上位 11%

理論・アルゴリズム 分野 142 件中 第 38 位

学術的評価 (35%)
92
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
19
コミュニティの注目度 (10%)
69
公開情報の充実度 (15%)
55

使用した入力: 収録ランク：CCF B, ICORE B, QUALIS A2 · 確認できる開催回数：1 · 会伴でフォローしている研究者：78 人 · 過去 24 か月にこのページを開いた研究者：34 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 80% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-10-09

論文募集

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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