'Lossless' presentation slideshows

Normal Forms

Normal Forms. Functional dependencies. Our goal: given a set of FD set, F, find an alternative FD set, G that is: smaller equivalent. Bad news: Testing F=G (F+ = G+) is computationally expensive. Good news:

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Normalization

Normalization. Canonical Cover. Sets of functional dependencies may have redundant dependencies that can be inferred from the others For example: A ? C is redundant in: { A ? B , B ? C } Parts of a functional dependency may be redundant

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IT-501 Database Management Systems

IT-501 Database Management Systems. By- Jesmin Akhter Assistant Professor, IIT, Jahangirnagar University. Lecture 07 Relational Database Design Normalization-Part-1. Outline. Overview of Relational DBMS Normalization(1 st lecture). Normalization.

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????? ???? ???? ??

????? ???? ???? ??. Reduce the size of data. Reduces storage space and hence storage cost. Compression ratio = original data size/compressed data size Reduces time to retrieve and transmit data. Lossless And Lossy Compression. compressedData = compress(originalData)

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Schema Refinement and Normalization

Schema Refinement and Normalization. Nobody realizes that some people expend tremendous energy merely to be normal. Albert Camus. Functional Dependencies (Review). A functional dependency X ? Y holds over relation schema R if, for every allowable instance r of R:

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Outline: Lossless -join (15.1.3) Basic definition of Lossless -join Examples Testing algorithm

Outline: Lossless -join (15.1.3) Basic definition of Lossless -join Examples Testing algorithm. Basic definition of Lossless -join A decomposition D = {R 1 , R 2 ,..., R m } of R has the lossless

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On the Use of Standards for Microarray Lossless Image Compression

On the Use of Standards for Microarray Lossless Image Compression. Author :Armando J. Pinho*, Antonio R. C.Paiva, and Antonio J. R. Neves Source :IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,VOL.53, NO. 3, MARCH 2006 Speaker: Ren-Li Shen. Outline. Introduction

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ICS 321 Fall 2010 Normal Forms (ii)

ICS 321 Fall 2010 Normal Forms (ii). Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa. Redundancies & Decompositions. Hourly_Emps. Hourly_Emps. RatingWages. Decompositions.

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Bayesian Enhancement of Speech Signals

Bayesian Enhancement of Speech Signals. Jeremy Reed. Outline. Speech Model Bayes application MCMC algorithm Results. Speech Model. Predict current speech sample from p previous samples (AR process) Justified by physics Lossless acoustic tubes Time for vocal tract to change shape

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THE THEORY & CONCEPTS

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Comparison of image compression algorithms

Comparison of image compression algorithms. ECE-533 Paula Aguilera. Five Different Formats. Lossless (24 bit color). TIFF-LZW 378KB. BMP 696 KB. JPEG 43,3KB. PNG 258KB. Lossless(8 bit color). BMP 257 KB. TIFF LWZ 251KB. PNG 173 KB. JPEG 79 KB. Text compression:.

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abs(a)<abs(b)?

abs(a)<abs(b)?. swap bsSymbit1=1. bsSymbit1=0. a-b<0 || ( a==b && a<0 && b<0 )?. negative bsSymbit0=1. bsSymbit0=0. a+b even?. s =( a+b )/2 d=(a-b)/2. s=(2*lav+1-( a+b ))/2 d = s+b. parameter sets. parameter bands. 71 hybrid subbands. 0. 1. Coding a frame:.

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Image Compression

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