Image Dimensions:

Image Dimensions:
The dimension image is defined as the width and height of that image. I.e. number of horizontal pixels (width) and the number of vertical pixels (height).
Example:

If an image has the dimension 1200x1600 means its width is 1200 pixels and the height is 1600 pixels. The dimension 80x60 and 200x150 are given below as the sample images.

80x60

200x150

Higher the image dimensions higher the image size. The image dimension is available at Right Click Image -> Properties ->Advanced -> summery.

Image Resolution: Dots per Inch (DPI)
It is defined as the number of pixels per inch. Pixels are also called as dots. Dots per inch represent how many dots or pixels available in an inch. It varies image to image and hardware to hardware which displays the image.
DPI for CRT Monitors and LCD Displays:
The commercial CRT monitors and LCD displays have 96 Dots per inch. To get the DPI values for normal CRT monitors Right Click in Desktop -> Properties -> Settings tab-> Advanced. The following image shows an CRT monitor DPI as 96 as normal size.

DPI for Printers :
The printers DPI varies from 200 to 300 depends on the printers. Normally the DPI value for printers would be 300. If we need the output image size is 3x4 inch then the image dimension would be 900x1200 for 300 DPI printer.
 
 

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Image Bit Depth or Color Depth of an Image.

Image Bit Depth or Color Depth of an Image.

The number of bits required to store each pixel’s color information. It depends on the image formats and the numbers of colors the images have. It ultimately shows the number of unique colors available in the image. Higher the bit depths have the high image quality i.e. higher the bit depth or color depth the number unique color is high.

For example.

If the Color depth of an image is 8 means it has 256 unique colors.

       Color depth = 8;

      Number of unique colors = 2^8 = 256

Depends on the number of colors bit depth can be classified as the following

•  1 bit images
•   2 bit images
•   3 bit images
•   4 bit images
•   5 bit images
•   6 bit images
•   7 bit images
•   8 bit images
•   24 bit images
•   32 images
•   48 images

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RLE Encoding

RLE – Run Length Encoding:

It is a famous lossless compression algorithm widely used in many data compression applications and used with many image compression algorithms. In Run Length Encoding method the consecutive same data is compressed. This technique used in few type of BMP image formats particularly 8 bit and 16 bit BMP images, JPEG images and almost all compression application software like winzip, winrar etc..

RLE – Run Length Encoding Example.

Input data : qqqqqqqqqqwwwrrrrqqqqqqqiiioooopppppp
RLE compressed Data : 10q3w4r7q3i4o6p

Let’s consider the above sample data, in RLE compression the count and the data value is represented. i.e q is repeated at 10 times instead of writing ten q it can be written as 10q. likewise all other data’s are written.

Compression ratio = (37 (total number of chars in input data) - 15 (required no of chars to represent the data) ) / 37 * 100
= 59.5 %

Compression ratio for above case is 59.5 %;

Input data : qwertyqwerty
RLE compressed Data : 1q1w1e1r1t1y1q1w1e1r1t1y

In the above sample the compression ratio is -100%. I.e. negative compression. From the above two example RLE compression is good only when the data is repeated one. Otherwise it would give negative compression.

Advantages :

1. During implementation just one pass is required to compress the input data.
2. Easy understanding.
3. Less memory requirement.
4. High compression ratio on repeated data input.

Disadvantages:

1. If the data is not repeated one then the compression ratio would be negative.

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ARITHMETIC CODING

ARITHMETIC CODING:

In arithmetic coding, Input data is represented by an interval of real numbers between 0 and 1. As the input data is large, the interval required to represent is smaller, i.e. the number of bits required to specify the interval increases. Successive symbols of the input data reduce the size of the interval according with the symbol probabilities generated by the arithmetic encoding. The repeated symbols reduce the range by less than the unlikely symbols and hence add fewer bits to the input data. The range of input data is the entire interval zero to one (0, l), which denotes the half-open interval 0.5 <> 1

ARITHMETIC CODING Example:

The Fixed model (alphabet is (a, e, i, o, u,!)) is used with the following probabilities shown in the Table.

Let’s take the input data become eaii!. Initially, both encoder and decoder know that the range is (0, 1).

From first symbol, e, the encoder narrows it to [0.2, 04],
The second symbol, a, will narrow this new range to the first one-fifth of it. Like the following









Representation of the Arithmetic Coding Process:


Suppose the input data is like a, aa, aaa, aaaa, the encoded number will be 0.0. To solve this ambiguity, In data model each data ends with a special EOF symbol known to both encoder and decoder. In the above table ! would be the EOF.

The Entropy of eaii! Is
-log0.3 – log0.2 – log0.1 – log0.1 – log0.1
= - log0.00006 ≈ 4.22

Arithmetic Encoding Algorithm:

/* Call Encode_Data repeatedly for each symbol in the input data. */
/* Ensure that a distinguished "terminator" symbol is encoded last, then */
/* transmit any value in the range [low, high]. */

Encode_Data(symbol, cum-freq)
range = high - low
high = low + range*cum-freq [symbol-1]
low = low + range*cum-freq [symbol]

/* ARITHMETIC DECODING ALGORITHM. */
/* "Value" is the number that has been received. */
/* Continue calling Decode_Data until the terminator symbol is returned. */

Decode_Data(cum-freq)
find symbol such that
cum-freq[symbol] <= (value-low)/(high-low) <>

range = high - low

high = low + range*cum-freq[symbol-1]

low = low + range*cum-freq[symbol]

return symbol

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