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Comprehensive Overview of Image and Text Compression Methods

Learn about compression techniques such as Run-Length Encoding, Huffman Coding, predictive coding, and more for images and text documents. Understand concepts like entropy, vector space models, relevance feedback, and Precision and Recall. Explore methods for efficient data storage and transmission.

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Comprehensive Overview of Image and Text Compression Methods

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  1. Compression • Word document: 1 page is about 2 to 4kB • Raster Image of 1 page at 600 dpi is about 35MB • Compression Ratio, CR = , where is the number of bits • Compression techniques take advantage of: • Sparse coverage • Repetitive scan lines • Large smooth gray areas • ASCII code, always 8 bits per character • Long words frequently used

  2. Entropy • Entropy is a quantitative term used for amount of information in a string 1.00 0.80 0.60 0.40 0.20 0.00 H(1)+H(0) H(1) H(0) 0.0 0.2 0.4 0.6 0.8 1.0 For N clusters, where li is the length of the ith cluster

  3. Binary Image Compression Techniques • Packing: 8 pixels per byte • Run Length Encoding: Assume 100 dpi, 850 bits per line • encode only the white bits as they are long runs • Top part of a page could be 0(200)111110(3)111110(3) …. • Huffman Coding: use short length codes for frequent messages Encode Decode

  4. 0 (2,7) (13,2) 0 (2,7) (13,2) 0 (2,7) (13,2) 0 (2,2) (7,2) (13,2) 0 (2,2) (7,2) (13,2) 0 (2,7) (13,2) 0 (2,2)(7,2)(13,2) 0 (2,2)(7,2)(13,2) 0 0 Bit map: 160 bits 50 numbers in range 0-15 Use 4 bits per number: 200 bits 2 bits per symbol: 100 bits HC: 1.84 x 50 = 92 bits Huffman Encoding

  5. Predictive Coding • Most pixels in adjacent scan lines s1 and s2 are the same • S2’ is the predicted version 2 dimensional prediction • Probabilities gathered from document collections • Tradeoff between context size and table size; Context size of 12 pixels common which uses a 4096 entries table

  6. Group III Fax • White runs and black runs alternate • All lines begin with a white run (possibly length zero) • There are 1728 pixels in a scan line • Makeup codes encode a multiple of 64 bits • Terminating codes encode the remainder (0 to 63) • EOL for each line • CCITT lookup tables • Example, • White run of 500 pixels would be encoded as • 500 = 7x 64 + 52 • Makeup code for 7x 64 is 0110 0100 • Terminating code for 52 is 0101 0101 • Complete code is 0110 0100 0101 0101

  7. Group IV READ b1 b2 Reference Coding a0 a1 a2 • a0 is the reference changing pixel; a1 is the next changing pixel after a0; and a2 is the next changing pixel after a1. • b1 is the first changing pixel on the reference line after a0 and is of opposite color to a0; b2 is the next changing pixel after b1. • To start, a0 is located at an imaginary white pixel point immediately to the left of the coding line. • Follow READ algorithm chart

  8. Group IV READ

  9. Grayscale Compression- JPEG

  10. Information Retrieval (Typed text documents) • IR goal is to represent a collection of documents were a single document is the smallest unit of information • Typify document content and present information upon request Similarity Measure Requests Documents • OCR translates images of text to computer readable form and IR extracts the text upon request • Inverted Index: Transpose the document-term relationship to a term-document relationship • Remove Stopwords: the, and, to, a, in, that, through, but, etc. • Word Stemming: Remove prefixes and suffixes and normalize

  11. Query 1: recognition or retrievalResponse: 1 2 3 Query 2:sequentially and readableResponse: 3 Query 3:not translateResponse: 2 Query:character and recognition or retrieval

  12. Vector Space Model • Each document is denoted by a vector of concepts (index terms) • If the term is present in the document 1 is placed in the vector • Vector of document 1 from table: (1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 1) • Weighting: Favor terms with high frequency in a few documents N = total documents Dfi = no. of docs containing term i Tij = frequency of term i in doc j Document similarity measure between Dj (wi,w2j,…wmj) and Qr (q1r,q2r,..qmr)

  13. Relevance Feedback N = no. of documents in collection R = number of documents relevant to query q N = no. of documents containing t R = no. of relevant documents containing t F =proportion of relevant documents to non-relevant documents in which term occurs F’ = without relevance feedback k = constant, adjusted with collection size c = collection size fi = no. of documents in which term i occurs tij = frequency term i in document j Maxtfj = maximum term frequency in document j

  14. Precision and Recall • Coverage: extent to which system includes relevant documents • Time lag: average time it takes to produce an answer to a search request • Presentation: quality of the output • Effort: energies put forth by user to obtain information sought • Recall: proportion of relevant material received from a query • Precision: proportion of retrieved documents actually relevant Recall= Precision=

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