منابع مشابه
Off-Line Dictionary-Based Compression The dictionary-based compression methods
The dictionary-based compression methods described in Chapter 3 of the book are different, but have one thing in common; they generate the dictionary as they go along, reading data and compressing it. The dictionary is not included in the compressed file and is generated by the decoder in lockstep with the encoder. Thus, such methods can be termed " online. " In contrast, the methods described ...
متن کاملImproved Compression-Latency Trade-Off via Delayed-Dictionary Compression
We have recently introduced a novel compression algorithm for packet networks: delayed-dictionary compression, which enables an improved compression-latency trade-off. By allowing delay in the dictionary construction, the algorithm handles effectively the problems of packet drops and packet reordering: Its compression quality is close to that of streaming compression (and is substantially bette...
متن کاملRevisiting dictionary-based compression
One of the attractive ways to increase text compression is to replace words with references to a text dictionary given in advance. Although there exist a few works in this area, they do not fully exploit the compression possibilities or consider alternative preprocessing variants for various compressors in the latter phase. In this paper, we discuss several aspects of dictionary-based compressi...
متن کاملDictionary-Based Tree Compression
Trees are a ubiquitous data structure in computer science. LISP, for instance, was designed to manipulate nested lists, that is, ordered unranked trees. Already at that time, DAGs were used to detect common subexpression, a process known as “hash consing.” In a DAG every distinct subtree is represented only once (but can be referenced many times) and hence it constitutes a dictionary-based comp...
متن کاملImage compression with on-line and off-line learning
Images typically contain smooth regions, which are easily compressed by linear transforms, and high activity regions (edges, textures), which are harder to compress. To compress the first kind, we use a “zero” encoder that has infinite context, very low capacity, and which adapts very quickly to the content. For the second, we use an “interpolation” encoder, based on neural networks, which has ...
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ژورنال
عنوان ژورنال: Proceedings of the IEEE
سال: 2000
ISSN: 0018-9219,1558-2256
DOI: 10.1109/5.892708