摘要翻译：
提出了一种新的数据压缩方法，并将其应用于多媒体内容。该方法将消息分离成既适用于无损编码又适用于“有损”或统计编码技术的组件，通过分别对信号和噪声进行编码来压缩复杂的对象。这可以通过精确压缩数据的最重要位来证明，因为它们通常是冗余的和可压缩的，或者对剩余位拟合一个最大可能的噪声函数，或者使用有损方法来压缩它们。在解压缩时，有效比特被解码并添加到噪声函数，无论是从噪声模型采样还是从有损码解压缩。这会产生与原始数据相似的压缩数据。对于许多测试图像，使用JPEG2000进行有损编码和使用PAQ8l进行无损编码的两部分图像编码比等长的JPEG2000产生更小的均方误差。计算机生成的图像通常使用这种方法比通过直接有损编码压缩得更好，许多黑白照片和大多数彩色照片在足够高的质量水平上也是如此。最后给出了该方法在音视频编码中的应用实例。由于两部分码对周期和混沌数据都是有效的，所以大致相似的对象的串联可以被有效地编码，这导致改进的推理。在人工智能中的应用表明，使用经济的无损代码的信号具有临界水平的冗余，这导致比编码不充分的数据或太多细节的信号更好的基于描述的推理。
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英文标题：
《Critical Data Compression》
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作者：
John Scoville
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Multimedia        多媒体
分类描述：Roughly includes material in ACM Subject Class H.5.1.
大致包括ACM学科类H.5.1中的材料。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  A new approach to data compression is developed and applied to multimedia content. This method separates messages into components suitable for both lossless coding and 'lossy' or statistical coding techniques, compressing complex objects by separately encoding signals and noise. This is demonstrated by compressing the most significant bits of data exactly, since they are typically redundant and compressible, and either fitting a maximally likely noise function to the residual bits or compressing them using lossy methods. Upon decompression, the significant bits are decoded and added to a noise function, whether sampled from a noise model or decompressed from a lossy code. This results in compressed data similar to the original. For many test images, a two-part image code using JPEG2000 for lossy coding and PAQ8l for lossless coding produces less mean-squared error than an equal length of JPEG2000. Computer-generated images typically compress better using this method than through direct lossy coding, as do many black and white photographs and most color photographs at sufficiently high quality levels. Examples applying the method to audio and video coding are also demonstrated. Since two-part codes are efficient for both periodic and chaotic data, concatenations of roughly similar objects may be encoded efficiently, which leads to improved inference. Applications to artificial intelligence are demonstrated, showing that signals using an economical lossless code have a critical level of redundancy which leads to better description-based inference than signals which encode either insufficient data or too much detail.
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PDF链接：
https://arxiv.org/pdf/1112.5493