Image compression techniques

Usually images take a large amount of space to store and therefore the use of bandwidth also high for the transmission. In order to avoid this (reduce storage space & increase transmission speed) different image compression methods can be used.

How an Image is stored ? 


Basically, the image compression methods can be classified into 02 categories depending on whether having exact image details on compressed image as original one or not.

  1. Loseless technique
  2. Lossy technique

Loseless technique

In this technique, the compressed image has exact image details perfectly as in the original image without any loss. This is also called noiseless, because it add no noise to final image. This is also known as entropy coding since it use statistics & decomposition techniques to avoid redundancy which may occur during the compression. Loseless image compression is rare in use but it is mainly used in medical imaging.

Techniques used in Loseless compression

    • Run length encoding
    • Huffman encoding
    • LZW coding
    • Area coding

Run length encoding

This is a very simple compression method used for sequential data. It is very useful in case of repetitive data. This technique replaces sequences of identical symbols (pixels) ,called runs by shorter symbols. The run length code for a gray scale image is represented by a sequence { Vi , Ri } where Vi is the intensity of pixel and Ri refers to the number of consecutive pixels with the intensity Vi as shown in the figure. If both Vi and Ri are represented by one byte, this span of 12 pixels is coded using eight bytes yielding a compression ration of 1: 5.

Huffman Encoding

This is a general technique for coding symbols based on their statistical occurrence frequencies (probabilities). The pixels in the image are treated as symbols. The symbols that occur more frequently are assigned a smaller number of bits, while the symbols that occur less frequently are assigned a relatively larger number of bits. Huffman code is a prefix code. This means that the (binary) code of any symbol is not the prefix of the code of any other symbol. Most image coding standards use lossy techniques in the earlier stages of compression and use Huffman coding as the final step.

LZW Coding

LZW (Lempel- Ziv – Welch ) is a dictionary based coding. Dictionary based coding can be static or dynamic. In static dictionary coding, dictionary is fixed during the encoding and decoding processes. In dynamic dictionary coding, the dictionary is updated on fly. LZW is widely used in computer industry and is implemented as compress command on UNIX.

Area Coding

Area coding is an enhanced form of run length coding, reflecting the two dimensional character of images. This is a significant advance over the other lossless methods. For coding an image it does not make too much sense to interpret it as a sequential stream, as it is in fact an array of sequences,building up a two dimensional object. The algorithms for area coding try to find rectangular regions with the same characteristics. These regions are coded in a descriptive form as an element with two points and a certain structure. This type of coding can be highly effective but it bears the problem of a nonlinear method, which cannot be implemented in hardware. Therefore, the performance in terms of compression time is not competitive, although the compression ratio is.


Lossy technique

In lossy image compression technique the compression ratios are higher than in loseless. This technique is widely used because compressed image is appropriate for most ordinary applications. But in this method, compressed image is not identical to the original image  but reasonably close to it.

Things to consider in Lossy method 

    • Compression ratio
    • Signal – to – noise ratio
    • Speed of encoding & decoding

Techniques used in Lossy compression

    • Transformation coding
    • Vector quantization
    • Fractal coding
    • Block Truncation Coding
    • Subband coding

Transformation Coding

In this coding scheme, transforms such as DFT (Discrete Fourier Transform) and DCT (Discrete Cosine Transform) are used to change the pixels in the original image into frequency domain coefficients (called transform coefficients).These coefficients have several desirable properties. One is the energy compaction property that results in most of the energy of the original data being concentrated in only a few of the significant transform coefficients. This is the basis of achieving the compression. Only those few significant coefficients are selected and the remaining are discarded.The selected coefficients are considered for further quantization and entropy encoding. DCT coding has been the most common approach to transform coding.It is also adopted in the JPEG image compression standard.

Vector Quantization

The basic idea in this technique is to develop a dictionary of fixed-size vectors, called code vectors. A vector is usually a block of pixel values. A given image is then partitioned into non-overlapping blocks (vectors) called image vectors. Then for each in the dictionary is determined and its index in the dictionary is used as the encoding of the original image vector. Thus, each image is represented by a sequence of indices that can be further entropy coded.

Fractal Coding

The essential idea here is to decompose the image into seg- ments by using standard image processing techniques such as color separation, edge detection, and spectrum and texture analysis. Then each segment is looked up in a library of fractals. The library actually contains codes called iterated function system (IFS) codes, which are compact sets of numbers. Using a systematic procedure, a set of codes for a given image are determined, such that when the IFS codes are applied to a suitable set of image blocks yield an image that is a very close approximation of the original. This scheme is highly effective for compressing images that have good regularity and self-similarity.

Block truncation coding

In this scheme, the image is divided into non overlapping blocks of pixels. For each block, threshold and reconstruction values are determined. The threshold is usually the mean of the pixel values in the block. Then a bitmap of the block is derived by replacing all pixels whose values are greater than or equal (less than) to the threshold by a 1 (0). Then for each segment (group of 1s and 0s) in the bitmap, the reconstruction value is determined. This is the average of the values of the corresponding pixels in the original block.

Sub band coding

In this scheme, the image is analyzed to produce the components containing frequencies in well-defined bands, the sub bands. Subsequently, quantization and coding is applied to each of the bands. The advantage of this scheme is that the quantization and coding well suited for each of the sub bands can be designed separately.

Source – Internet


Posted on March 4, 2013, in Other and tagged , , , , . Bookmark the permalink. Leave a comment.

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