Comparative Analysis of Huffman and Arithmetic Coding Algorithms for Image Compression

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 09 | Sep 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 16
Comparative Analysis of Huffman and Arithmetic Coding Algorithms
for Image Compression
Swetha Vura1,2, Yamuna Devi C.R3
1Assistant Professor, Dept. of ECE, School of Engineering and Technology, CMR University, Bengaluru, India
2Research Scholar, VTU, Belagavi, India
3Associate Professor & HOD, Dept. of ETE, Dr. Ambedkar Institute of Technology, Bengaluru, India
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Abstract - Images are one of the most important visual
representations used almost in every field. They require
memory for their storage which necessitates a lot of space.
Image compression plays a pivotal role in reducing the size of
an image so that more images can be stored and thereby
increasing the transmission speed. Many coding algorithms
are written to compress images and reduce redundancy. This
paper analyses and compares Huffman and Arithmeticcoding
algorithms with reference to image compression.
Key Words: Image compression, Huffman coding,
Arithmetic coding, compression ratio, compression time
1. INTRODUCTION
Image is represented as a 2D matrix in terms of rows and
columns. The intersection of a particular row and column is
called a pixel. To store these images, lot of memory and
storage space is required. This gives rise to the need for
image compression where the image size is reduced to
accommodate more images. As the resources are finite, the
techniques used in image compression have to effectively
employ the storage space andthebandwidth[1].Imagesfind
their application in almost every possible field like satellites
[2-5], medical [6,7], artificial intelligence (AI), machine
learning, IOT, robotics, computer vision, patternrecognition
etc. The real challenge is to decrease the size without
deteriorating the quality of the image. Joint Photographic
Experts Group (JPEG) is a standard for compression [8]
which comprises of steps including transformation,
quantization and coding processes. The major classification
of image compression is lossy and lossless compression
techniques [9]. In lossy technique, we have data loss but in
lossless, the decompressed and original image are identical
[10,11]. Some of the lossless compression methods [12,13]
are Shannon Fano Coding, Run-length Encoding, Huffman
Coding, Arithmetic Coding, Lempel Ziv Coding etc. The
coding algorithms are assessed [14] on various aspects like
compression ratio, compression time, Peak signal-to-noise
Ratio (PSNR) etc.
2.COMPARISON BETWEEN HUFFMAN AND
ARITHMETIC CODING ALGORITHMS
Huffman coding is a type of entropy encoding algorithm
which finds the improved methodofencodingstringsofdata
depending on their respective frequency. On the basis of the
assigned frequency, the minutest variable length code is
designated to each character based on its frequency of
occurrence in the data. This coding incorporates a distinct
method for selecting the representation for each symbol,
which results in a prefix-free code [15] where the more
recurrently used characters are allotted smaller codes and
less recurrently used characters are allotted larger codes.
Huffman algorithm builds an expanded binary tree of
minimal weighted path length from a series of weights. This
list consists of probabilities of symbol occurrences. This
Huffman tree is used to define variable sized
representations.
Arithmetic codingcircumventsthethoughtofsubstituting an
input symbol with a distinct code. The less frequently used
symbols are represented withmore bitsandmorefrequently
used symbols are represented with less bits [16]. The
channel of input symbols is substituted with a single point
floating number which lies between 0 and 1. This unique
number can be decoded to construct the exact channel of
symbols that went into its design (Fig. 1).
Fig -1: Block Diagram of Arithmetic Coding Algorithm [16]

3. LITERATURE SURVEY
The efficiency of Adaptive Huffman and Arithmetic codingis
compared for different sets of images in [17]. An application
named CMedia Compressor is proposed where analysis is
performed on a quantitative basis. Thesampleimageswhich
were selected for the process of compression are of nature
and architecture and taken from the public domain. The
proposed application provides the optiontoselect either the
Adaptive Huffman Coding or Arithmetic Coding. The
parameters calculated are compression time and space-
savings to determine the efficiency of the coding algorithms.
The simulation results conclude that Arithmetic coding has
less compression time for all the sample images and high
space-savings parameter for most of the cases. For text files,
Adaptive Huffman coding performed better compared to
Arithmetic coding.
A hybrid combination of DWT and DCT is usedtoanalyse the
performance of Huffman and Arithmetic Coding in [18]. The
color images of Lena and Baboon are used for the process of
compression as depicted in Fig. 2. Theoriginal imageisLevel
3 decomposed by Discrete Wavelet Transform and the
quantized sub bands are encoded with Huffman and
Arithmetic coding techniques. Then the 2D DCT is applied to
get the compressed image. This compressed image has to
undergo inverse transformation using IDCT and decoded
along with inverse DWT to obtain the decompressed image.
On calculating PSNR and Compression Ratio (CR), it is
inferred that HuffmancodinghashigherPSNR incomparison
to Arithmetic coding but has less CR.
Fig -2: Flowchart of the process of compression and
decompression [18]
Stereoscopic images taken in 3D have more depth and need
more storage space in contrast to normal images.
Compression of such images becomes a herculean task. The
proposed method in [19] compares the results of
stereoscopic compression using Arithmetic coding (SCAC)
and stereoscopiccompressionusingHuffmancoding(SCHC).
In the SCAC method, the left and right images are initially
read to extract the RGB components. After quantization,
Arithmetic coding is applied to both the images. The CR is
first calculated. The PSNR value is obtained when the
decoding and retrieving process is completed. The same
procedure is similarly carried out for SCAC. Both the coding
approaches are analysed for lossy as well as lossless
techniques. The time taken for execution of SCHC is much
more than SCAC. It can also be noticed that Lossy SCAC gives
more CR and Lossless SCAC produces more PSNR.
The images captured by the satellite in the raw format
occupy enormous amount of memory and are complex in
nature. The images used for remote sensing are generally
multispectral images which are large in size and hence
require a lot of space for storage. In [20], a hybrid
methodology is proposed which consists of Lempel-Ziv-
Welch (LZW) Coding and Arithmetic Coding to compress
such images. The simulation results for the proposed hybrid
method are analysed in comparison with other lossless
methods like Run Length Coding, Huffman coding etc. The
multispectral image is first applied with LZW encoding. On
the resultant encoded image, Arithmetic encoding is
employed. Once the bitstream is compressed, it is decoded
with Arithmetic and LZW methods. The deconstructed
images (Fig. 3) are evaluated with parameterslikeCR,PSNR,
Bits per Pixel (BPP) and Structural Similarity Index (SSIM).
The results indicate that the adopted hybrid method
performs better compared to traditional compression
methods. Higher compression ratio and less BPP are
achieved in the proposed hybrid methodology.
Fig -3: Resultant multispectral images of hybrid
methodology [20]
Hospitals generate enormous data daily and therefore
necessitate a lot of storage space.These medical imageshave
to be compressed so that more number of images can be
stored. Lossless image compression algorithms like Run-
length encoding, LZW, Shannon-Fano, Huffman and

Arithmetic coding are analysed in [21]. RLE performs well
only if the image has long runs of similar data. LZW is a
lossless compression algorithm but is complex and
consumes a lot of time. In Shannon-Fano coding, the
generated output code may vary each time. There is a huge
loss of data which is not acceptable.Huffmancodingadoptsa
bottom-up approach where an optimal prefix-free code is
generated (Fig. 4). The major disadvantage is that even if
there is a small change in the code, the entire messageislost.
A fixed number of bits are used in Arithmetic coding which
gives better compression ratio but increases the
compression time. It is concluded that Huffman coding
surpasses other algorithms for real time applications.
Fig -4: Encoding of Huffman tree [21]
Digital Image Processing has a plethora of applications
which include remote sensing, medical, robotics, machine
learning, artificial intelligence, pattern recognition etc. The
algorithms and techniques required for image compression
are analysed in [22]. JPEG compression inputs an image to
subdivide it as n x n images. These sub-images are
transformed using algorithms like DCT, DWT etc. They are
then quantized where there is a particular loss of data. The
quantized output is encoded using techniques like Run-
length, Huffman, Arithmetic coding etc. It is observed that
Huffman coding [23] has a faster compression rate while
Arithmetic coding gives higher compression ratio [24, 25].
Huffman coding and Arithmetic coding are extensively used
in the field of image compression. As both the coding
techniques have their own limitations, an effective and
alternate algorithm is proposed in [26] which is based on
Huffman coding. Initially Huffman coding algorithm is
employed to the data (Fig. 5) and then the resultant data is
read whether it is long or short. The short bits are ignored
and if the data has more number of bits,furthercompression
takes place. Depending on whether the current and the next
character are in the same leaf, the flag bits “1” and “0” are
added. The addition of the flag bits ensure that the bits can
either be truncated or writtendirectly,avoiding repetitionof
data. An increasedcompressionratioof15.30% wasattained
when compared toHuffman codingalgorithm.Thisproposed
algorithm was less complex and produced greater
compression ratios than Huffman coding with the same
compression time.
Fig -5: Flow diagram of proposed algorithm [26]
4. CONCLUSION
With the growing data and information in images in every
field, image compression is inevitable. Huffman coding and
Arithmetic coding have been applied and tested on a diverse
set of images from satellite, multispectral, medical,
architectural, stereoscopy etc. It has been observed that
Arithmetic coding has higher compression ratio but
increased compression time in comparison to Huffman
coding. In terms of performance, Huffman coding surpasses
Arithmetic coding with higher PSNR. Some of the hybrid
methodologies have also been discussed in this paper which
show better performance than traditional methods.
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