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Short note on: JPEG in image compression.

1. JPEG 2000 standard for the compression of still images is based on the Discrete Wavelet Transform (DWT). This transform decomposes the image using functions called wavelets.
2. The basic idea is to have a more localized analysis of the information which is not possible using cosine functions whose temporal or spatial supports are identical to the data.
3. JPEG 2000 Advantages:
4. Better image quality that JPEG at the same file size or alternatively 25-35 % smaller file sizes with the same quality.
5. Good image quality at low bit rates ( even with compression ratios over 80 :1)
6. Low complexity option for devices with limited resources.

7. Scalable image files – no decomposition needed for reformatting with JPEG 2000, the image that best matches the target device can be extracted from a single compressed file on a server. Options include:

   a) Image sizes from thumbnail to full size.

   b) Grayscale to full 3 channel color.

   c) Low quality image to lossless (identical to original image)

8. JPEG 2000 is more suitable to web-graphics than baseline JPEG because it supports alpha-channel (transparency component)

9. Region of Interest (ROI): One can define some more interesting parts of image, which are coded with more bits than surrounding areas.

$$\text{Figure 3.2.1 Block Diagram of JPEG 2000}$$

A. Segment Generator :

• It checks the tone of image based on bpp value. It accepts color or grayscale images.
• It checks tone of image it generate variable length segment of image called ‘Tiles’. Because of this improved functionality JPEG 2000 can accept non standard image.
• It divides the complete image into multiple of 8 pixels called as block of size of 8 pixels called as block of size of 8 × 8.
• If image is not complete multiples of 8 pixels then it adds extra zeros this process is called as zero padding.

B. Level Shifter:

• It converts unsigned value of an image into sign value of an image. This process increases the efficiency of discrete cosine/ wavelet transform.
• If bpp value is 8 bits, number of possible levels of an image is 256. i. e 28 ( Range 0 to 255). This unsigned range is converted into sign range of -128 to 127.

C. Discrete Wavelet Transform:

• It is localize image transform. Because of localize process LF and HF component are get scanned row wise as well as columnwise. Because of localize image transform image is divided in 4 blocks:

  LL: LF component Row and Columnwise

  LH: LF component Rowwise and HF component columnwise

  HL: HF component Rowwise and LF component columnwise

  HH: HF component Row and Column wise

• This DWT process is based on 2 dimensional subband coding

• 1 Dimensional Subband Coding given below:

$$\text{Figure 3.2.2 DWT Process}$$

• Above subband coding indicate nth order decomposition can be possible by using DWT where as $N^{th}$ order decomposition isn’t possible in baseline JPEG.
• By using DWT we can separate out regions of frequency components i.e Most of image information pushed towards extreme LL part.
• In case of image HH region of any decomposition can be rounded off.
• $N^{th}$ order decomposition is possible by using DWT.

$$\text{n = Number of or order of decomposition}$$

D. Quantizer

• Quantizer provide rounding off frequency domain image with respect to amplitude as well as co-ordinate of image the amount of rounding is described by fixed rounding table.
• The fractional value of frequency domain coefficient is rounded to the nearest possible integer is called amplitude rounding. In co-ordinate rounding it will round off unnecessary HF component of image.

E. Zig- Zag Encoder

• It converts 2 dimensional frequency domain image into one dimensional matrix. By using scanning process. The zigzag encoder doesn’t follow the natural scanning. It follows diagonal scanning as shown in figure below:

$$\text{Figure 3.2.4 Zig Zag Encoder}$$

• The diagonal scanning is based on Comet distribution because of this diagonal scanning the element in one dimensional matrix is also arranged frequency basis. The size of one dimensional area for each block 1 × 64.

F. Huffman Encoder

• It converts fixed length frequency domain pixel into variable length frequency domain pixel depending on probability of frequency domain pixel.
• The large amount compression in JPEG is achieved in Huffman encoder block. The blocks before the Huffman encoder are functions such a way that it will improve the compression ratio provided by Huffman encoder.

G. RLE

• The compression ratio provided by Huffman encoder is increased by run length encoding because in output of Huffman encoder there is in Run of zeroes or ones.