Hybrid JPEG Compression Using Histogram Based Segmentation

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Hybrid JPEG Compression Using Histogram Based

SegmentationM.Mohamed Sathik

1, K.Senthamarai Kannan

2and Y.Jacob Vetha Raj

3

1Department of Computer Science, Sadakathullah Appa College, Tirunelveli, [email protected]

2,3Department of Statistics, Manonmanium Sundaranar University, Tirunelveli, India

[email protected] [email protected]

Abstract-- Image compression is an inevitable solution for

image transmission since the channel bandwidth is limited andthe demand is for faster transmission. Storage limitation is alsoforcing to go for image compression as the color resolution andspatial resolutions are increasing according to qualityrequirements. JPEG compression is a widely used compression

technique. JPEG compression method uses linear quantization

and threshold values to maintain certain quality in an entireimage. The proposed method estimates the vitality of the blockof the image and adapts variable quantization and thresholdvalues. This ensures that the vital area of the image is highly

preserved than the other areas of the image. This hybridapproach increases the compression ratio and produces adesired high quality output image.

.

Key words-- Image Compression, Edge-Detection,

Segmentation. Image Transformation, JPEG,

Quantization.

I. INTRODUCTION

Every day, an enormous amount of information is stored, processed, and transmitted digitally.

Companies provide business associates, investors, andpotential customers with financial data, annual reports,inventory, and product information over the Internet. Andmuch of the online information is graphical or pictorial in

nature; the storage and communications requirements areimmense. Methods of compressing the data prior to storage

and/or transmission are of significant practical andcommercial interest.

Compression techniques fall under two broad

categories such as lossless[1] and lossy[2][3]. The formeris particularly useful in image archiving and allows the

image to be compressed and decompressed without losingany information. And the later, provides higher levels of data reduction but result in a less than perfect reproduction

of the original image. Lossy compression is useful inapplications such as broadcast television,videoconferencing, and facsimile transmission, in whichcertain amount of error is an acceptable trade-off forincreased compression performance. The foremost aim of image compression is to reduce the number of bits needed

to represent an image. In lossless image compressionalgorithms, the reconstructed image is identical to the

original image. Lossless algorithms, however, are limitedby the low compression ratios they can achieve. Lossycompression algorithms, on the other hand, are capable of achieving high compression ratios. Though the

reconstructed image is not identical to the original image,

lossy compression algorithms obtain high compression

ratios by exploiting human visual properties.Vector quantization [4],[5] , wavelet transformation

[1] , [5]-[10] techniques are widely used in addition tovarious other methods[11]-[17] in image compression. The

problem in lossless compression is that, the compression

ratio is very less; where as in the lossy compression thecompression ratio is very high but may loose vitalinformation of the image. Some of the works carried out inhybrid image compression [18]-[19] incorporated different

compression schemes like PVQ and DCTVQ in a singleimage compression. But the proposed method uses lossycompression method with different quality levels based on

the context to compress a single image by avoiding thedifficulties of using side information for imagedecompression in [20].

The proposed method performs a hybrid

compression, which makes a balance on compression ratioand image quality by compressing the vital parts of the

image with high quality. In this approach the main subjectin the image is very important than the background image.Considering the importance of image components, and the

effect of smoothness in image compression, this methodsegments the image as main subject and background, thenthe background of the image is subjected to low quality

lossy compression and the main subject is compressed withhigh quality lossy compression. There are enormousamount of work on image compression is carried out both

in lossless [1] [14] [17] and lossy [4] [15] compression.Very few works are carried out for Hybrid Imagecompression [18]-[20].

In the proposed work, for image compression, the

edge detection, segmentation, smoothing and dilationtechniques are used. For edge detection, segmentation

[21],[22] smoothing and dilation, there are lots of work hasbeen carried out [2],[3]. A novel and a time efficientmethod to detect edges and segmentation used in the

proposed work are described in section II, section III givesa detailed description of the proposed method, the resultsand discussion are given in section IV and the concludingremarks are given in section V.

II. BACKGROUND

A. JPEG Compression

Components of Image Compression System (JPEG). Image compression system consists of three closelyconnected components namely

Source encoder (DCT based)

(IJCSIS) International Journal of Computer Science and Information Security,

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Quantizer

Entropy encoderFigure 2 shows the architecture of the JPEG encoder.Principles behind JPEG Compression. A common

characteristic of most images is that the neighboring pixelsare correlated and therefore contain redundant information.The foremost task then is to find less correlated

representation of the image. Two fundamental componentsof compression are redundancy and irrelevancy reduction.

Redundancy reduction aims at removing duplication fromthe signal source. Irrelevancy reduction omits parts of thesignal that will not be noticed by the signal receiver, namelythe Human Visual System (HVS). The JPEG compression

standard (DCT based) employs the use of the discrete cosinetransform, which is applied to each 8 x 8 block of thepartitioned image. Compression is then achieved by

performing quantization of each of those 8 x 8 coefficientblocks.Image Transform Coding For JPEG Compression

Algorithm.In the image compression algorithm, the input image is

divided into 8-by-8 or 16-by-16 non-overlapping blocks,and the two-dimensional DCT is computed for each block.The DCT coefficients are then quantized, coded, and

transmitted. The JPEG receiver (or JPEG file reader)decodes the quantized DCT coefficients, computes theinverse two-dimensional DCT of each block, and then putsthe blocks back together into a single image. For typical

images, many of the DCT coefficients have values close tozero; these coefficients can be discarded without seriouslyaffecting the quality of the reconstructed image. A twodimensional DCT of an M by N matrix A is defined asfollows

M-1 N-1

p q mn

m =0n=0

0 p M-1

0 q N-1

(2m +1)p (2n +1)qα α A cos cos ,

2M 2N pq B

where

1/ M , p = 0p =

2/M , 1 p M-1α

1/ N , q = 0q =

2/N , 1 q N-1α

The DCT is an invertible transformation and its inverse is

given byM-1 N-1

p q pq

m =0n=0

0 p M-1

0 q N-1

(2m +1)p (2n +1)qα α B cos cos ,

2M 2Nmn A

Where

1/ M , p = 0p =

2/M , 1 p M-1α

1/ N , q = 0q =

2/N , 1 q N-1α

The DCT based encoder can be thought of as essentiallycompression of a stream of 8 X 8 blocks of image samples.Each 8 X 8 block makes its way through each processing

step, and yields output in compressed form into the datastream. Because adjacent image pixels are highly correlated,the ‘forward’ DCT (FDCT) processing step lays thefoundation for achieving data compression by concentrating

most of the signal in the lower spatial frequencies. For atypical 8 X 8 sample block from a typical source image,

most of the spatial frequencies have zero or near-zeroamplitude and need not be encoded. In principle, the DCTintroduces no loss to the source image samples; it merely

transforms them to a domain in which they can be moreefficiently encoded.After output from the FDCT, each of the 64 DCT

coefficients is uniformly quantized in conjunction with acarefully designed 64 – element Quantization Table. At thedecoder, the quantized values are multiplied by thecorresponding QT elements to recover the originalunquantized values. After quantization, all of the quantized

coefficients are ordered into the “zig-zag” sequence asshown in figure 1. This ordering helps to facilitate entropyencoding by placing low-frequency non-zero coefficientsbefore high-frequency coefficients. The DC coefficient,

which contains a significant fraction of the total imageenergy, is differentially encoded.

Figure 1 Zig-Zag Sequence

The JPEG decoder architecture is shown if figure 3which is the reverse procedure described for compression.

B. Segmentation

Let D be a matrix of order m x n, represents the

image of width m and height n. The domain for Di,j is[0..255] , for any i=1..m, and any j=1.. n.

The architecture of segmentation using histogram

is shown in figure 4. To make the process faster the highresolution input image is down sampled 2 times. When theimage is down sampled each time the dimension is reduced

by half of the original dimension. So the final down sampled

image (D) is of the dimension 4m

x 4n

. The down sampledimage is smoothed to get smoothed gray scale image usingequation (1).

Si,,j = 1/9(Di-1,,j-1 +Di-1,,j +Di-1,,j+1 +Di,,j-1 +Di,,j +Di,,j+1 +Di+1,,j-1

+Di+1,,j +Di+1,,j+1 ) …(1)

AC77

AC07 AC01

DCC




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Figure 2. JPEG Encoder Block Diagram

Compressed

Image data

Figure 3. JPEG Decoder Block Diagram

Figure -4 Classifier

The histogram(H) is computed for the gray scale image(S).

The most frequently present gray scale value (Mh) isdetermined from the histogram by equation (2) and is shown

as indicated by a line in figure 5.

Mh = arg{max(H(x))} …(2)

The background value of the images is having the highest

frequency in the case of homogenous background. In orderto surmise background textures a range of gray levelvalues are considered for segmentation. The range is

computed using the equations (3) and (4).

L = max( Mh – 30,0) …(3)

U = min( Mh + 30,255) …(4)

The gray scale image S is segmented to detect the

background area of the image using the function given inequation (5)

Bi,j = (Si,j > L) and (Si,j < U) …(5)

Figure – 5

After processing the pixel values for background area is 1 inthe binary image B. To avoid the problem of oversegmentation the binary image is subjected to sequence of

morphological operations. The binary image is eroded withsmaller circular structural element (SE) to remove smallersegments as given in equation (6).

SE B B …(6)

Smooth theImage

(S)

DownSample

2 Times (D)

HistogramComputation

(H)

RangeCalculation

(L & U)

BinarySegmentation

(B)

UpSample

2 Times

8 X 8 blocks

FDCT Quantizer Entropy

Encoder

Quantizer

Table

Huffman

Table

Compressed

image data

Source image

Entropy

Decoder

Dequantizer IDCT

Quantizer

Table

Huffman

TableReconstructed

Image




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Then the resultant image is subjected to morphologicalclosing operation with larger circular structural element asgiven in equation (7).

SE B B …(7)

III. PROPOSED HYBRID JPEG COMPRESSION.

The input image is initially segmented intobackground and foreground parts as described in section

II.B Then the image is divided into 8x8 blocks and DCTvalues are computed for each block. The quantization isperformed according to the predefined quantization table.The quantized values are then reordered based on zig-zagordering method described in section II A. The lower values

of AC coefficients are discarded from the zig-zag orderedlist by comparing the threshold value selected by theselector as per the block’s presences identified by the

classifier. If the block is present in foreground area then thethreshold is set to a higher value by the selector, otherwise a

lower value for threshold is set by the selector. Afterdiscarding insignificant coefficients the remaining data are

compressed by the standard entropy encoder based on thecode table.

Algorithm

1. Input High Resolution Color image.

2. Down sample the input image 2 times.

3. Convert the down sampled image to gray scaleimage (G).

4. Find histogram (H) of the gray scale image.

5. Find the lower (L) and upper (U) gray scale value

of background area.

6. Find Binary segmented image (B) from the gray

scale image (G)

7. Up sample Binary image (B) two times.8. Divide the input image into 8x8 blocks

9. Find DCT coefficients for each blocks

10. Quantize the DCT coefficients

11. Discard lower quantized values based on the

threshold value selected by the selector.

12. Compress remaining DCT coefficients by Entropy

Encoder

The architecture of the proposed method is shown in figure6. The Quantization Table is a fixed classical table derivedfrom empirical results. The Quantizer quantizes the DCT

coefficients computed by FDCT. The classifier identifies theclass of each pixel by segmenting the given input image.The selector and limiter works together to find the discard

threshold limit. The entropy encoder creates compressedcode using the Code Table. The compressed image may bestored or transmitted faster than the existing method.

IV. RESULTS AND DISCUSSION

The Hybrid JPEG Compression method is implemented

according to the description in section III and tested with aset of test images shown in figure 8. The results obtained

from the implementation of the proposed algorithms areshown in figures 7, 9,10 and table I. Figure 7.a shows theoriginal input image. In Figure 7.b the segmented objectand background area is discriminated by black and white.

The compressed bit rates of the twelve test images arecomputed and tabulated in table 1. The low quality (LQ) andhigh quality (HQ) JPEG compression is performed and the

corresponding compression ratios(CR) and PSNR valuesare tabulated. The PSNR is higher for HQ and CR is higherfor LQ. The Hybrid JPEG compression performs HQcompression on main subject area and LQ compression onbackground area thus the PSNR value at main subject area is

the same for Hybrid JPEG and HQ JPEG. Figure 9 showsthe comparison of normalized CRs of Hybrid JPEG and HQJPEG, it is observed that almost all of the images arecompressed better than classical JPEG compression. Figure

10 shows how well the compression ratio is increased than

the classical JPEG compression method.

QuantizingTable

Input

Image

FDCT Quantizer

Classifier Selector Limiter

Code Table Entropy

Encoder

Compressed

Image

Figure 6. Hybrid JPEG compression method




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a)Input Image

b)Segmented Main Subject Area

Figure – 7 Input /Output

Table -1 Compression Ratio and PSNR values obtained by Hybrid JPEG and JPEG

Image LQ Hybrid Hybrid/HQ HQ

CR2-CR3CR1 PSNR1 CR2 PSNR2 PSNR@MainSubject

CR3 PSNR3

3 26.00 21.52 7.46 27.82 27.84 7.46 27.82 0.0000

5 25.29 22.09 6.80 28.87 28.93 6.80 28.87 0.0004

10 23.83 20.78 5.41 28.09 28.13 5.41 28.09 0.0031

4 24.33 22.08 6.61 31.13 31.20 6.60 31.13 0.0068

11 27.75 25.33 8.62 35.97 36.25 8.61 36.01 0.0198

9 24.92 21.62 6.58 30.45 30.56 6.56 30.51 0.0204

1 27.54 23.03 8.40 29.37 29.40 8.37 29.37 0.0276

7 24.85 17.71 4.01 23.38 23.43 3.92 23.38 0.0911

8 24.63 21.97 6.73 31.05 31.13 6.64 31.10 0.092112 29.18 22.73 5.92 28.70 29.07 5.61 28.93 0.3111

6 26.47 20.12 5.54 25.31 25.33 5.19 25.30 0.3449

2 22.84 20.40 7.91 27.93 28.21 7.25 28.06 0.6541




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Figure – 8 Test Images (1-12 from Left to Right and Top to Bottom)

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11 12

Images

N o r m a l i z e

d C R

HyBrid

HQ Lossy

Figure – 9 Normalized Compression Ratio Obtained for Test ImagesIncreased Compression Ratio by Hybrid JPEG Compression

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1 2 3 4 5 6 7 8 9 10 11 12

Images

C o m p r e s s i o n R a t i o

Figure – 10 Increased Compression Ratios by Hybrid Compression.

V. CONCLUSION

The compression ratio of Hybrid JPEG method is

higher than JPEG method in more than 90% of test cases. Inthe worst case both Hybrid JPEG and JPEG method givesthe same compression ratio. The PSNR value at the main

subject area is same for both methods. The PSNR value at

the background area is lower in Hybrid JPEG method whichis acceptable, since the background area is not vital. The

Hybrid JPEG method is suitable for imagery with largertrivial background and certain level of loss is permissible.




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ACKNOWLEDGEMENTS

The authors express their gratitude to University

Grant Commission and Manonmanium SundaranarUniversity for financial assistance under the FacultyDevelopment Program.

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Hybrid JPEG Compression Using Histogram Based Segmentation

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