A STUDY ON IMAGE
COMPRESSION TECHNOLOGY
Dr.PL. Chithra
Department of Computer Science, University of Madras, Chennai-600025, India.
E. Shalini
Department of Computer Science, University of Madras, Chennai-600025, India.
ABSTRACT
This paper presents the effective comparative study of lossless and lossy color image compression technique based on Huffman encoding. Image compression is an application of data compression that encodes the original image with few bits. The growth of multimedia technology, if fetces to consent about to storage and bandwidth of image transmission. The image is transformed into the corresponding frequency domain then the frequency samples in the domain are quantized by the JPEG (Joint Photographic Expert Group) standard quantization table and produces the bit stream.
The resultant bit streams are encoded by Huffman encoding method. The encoded bits are considered as a compressed image which is reduced the size of an original image. This study paper has been compared the efficiency of JPEG and JPEG2000. From the studied metrics of Compression Ratio and Quality of reconstructed image we concluded that the JPEG2000 is efficiency better than the JPEG.
Keywords
Image compression, Wavelet transform, Huffman coding, JPEG picture.
1. INTRODUCTION
A study on Shuyun et. al [1] presents the image compression based on Huffman encoding and color images. It focused in this work. in recent decades, since huge volumes of image data are transmitted over networks, image compression has become one of the most important research areas. This paper is highly required for the modern computers, especially multimedia computer systems, which has become a mainstream direction. Especially in current computer applications, many videos and audios have taken the form of digitization, leading to a large amount of data storage. However, the current development of science and technology is limited, and many hardware technologies cannot fully satisfy the requirements of computer storage resources, and the gap between the bandwidth and the bandwidth is still large, so the data must be compressed before the data storage and transmission, otherwise the storage and transfer of the computer system cannot be effectively carried out. Due to the existence of encoding redundancy, redundancy between pixels, and visual information redundancy in multimedia data, the original data can be compressed.
PL.Chithra[3] proposed data compression technology which is actually a series of re-encoding of the original data, eliminating redundant data in the original data, reducing the amount of data to a minimum, so as to achieve the purpose of compressing various media data such as images, audio, and video [1].
At PL.Chithra and Christoper Tamilmathi [4] present, the common multimedia compression methods are divided into lossless compression methods and lossy compression methods. Lossless compression compresses redundant parts of the original data. Using lossless compression, the original data can be completely recovered without any errors or distortions, that is, after compression and decompression, a copy of the original data is generated. As the typical double space technology, the compression ratio of various types of data and files on the hard disk. Due to the limitation of the compression ratio, the use of lossless compression alone cannot handle the storage and transmission problems of digital sound and video images in real time. Lossy compression is at the expense of certain information, so that a higher compression ratio can be achieved Dr.PL.Chithra and A.RoselinClara [5] presents, the lossy compression method is mostly used for images with higher pixels, video, or sound quality files. For this type of data compression, the compression ratio can be increased by tens or hundreds of times. Most image compression methods can take this approach, mainly JPEG, MPFG and other types of files.
A variety of compression techniques has been proposed in the recent years [2, 4, 5]. PL.Chithra and K.Srividhya [6] Similarly, prediction techniques have been widely used in many applications including video processing.
In order to achieve compression, vector quantization (VQ) is applied to the prediction errors. Vector quantization has become one of the most popular lossy image compression techniques due to its simplicity and capability to achieve high compression ratios. VQ involves finding the closest vector in the error codebook to represent a vector of image pixels. When the closest vector is identified, only the index of the vector in the codebook is transmitted thus achieving compression. It is found that higher PSNR values can be obtained by applying VQ on the prediction errors instead of on the original image pixels. Giurgiutiu et.al [10] proposed the color image compression becomes mandatory because
of the splendid use of color images in the field of multimedia and internet applications. In a digital true color, each color component is quantized with 8 bits, and so a color is specified with 24 bits. The Human visual system cannot differentiate many colors. A color image contains a lot of data redundancy and requires a large amount of storage space. Thus compression is required to reduce the storage and transmission cost of color images. When focusing the color image compression, various methods have been proposed. The common coding methods used in lossy compression are predictive coding and transform coding, which allow information to be lost in the compression process.
Although all data cannot be fully recovered after decompression, but the lost part of the image, whether the original image or the sound, has little effect on the understanding of the whole file, it can obtain a good compression ratio Hardman et.al [12]. In order to make products of different manufacturers compatible, all countries have attached great importance to the establishment of universal data compression standards.
Staszewski et.al [14] Currently, three data compression coding standards commonly used in multimedia systems are:
• JPEG standard (ISO CD 10918) for digital compression coding of continuous-tone still images;
• MPEG standard (ISO/IEC 11172), suitable for compression coding of moving pictures and accompanying sounds on digital storage media;
• CCITTH.261 standard, suitable for digital compression coding in application systems such as video telephony and conference television.
At present, Liu et.al [15] with the rapid development of the network, the diverse needs of users, such as the real-time transmission of streaming media, the compression and transmission of high resolution images, are largely dependent on the multimedia compression technology. Jack et.al [16] proposed the current image data compression technology cannot meet the needs of all kinds of network multimedia applications. Therefore, the research and application of multimedia technology in network transmission has become more and more active and attracted much attention, especially the focus of image data compression[1]. JPEG2000 is the latest achievement of image compression technology in this form. JPEG2000 can facilitate progressive transmission, JPEG2000 support lossy compression, also support lossless compression, good low bit rate compression performance and the protection of image security through watermark, markup, killings and encryption. It has been widely used in image compression on the network. Based on the JPEG2000 standard, this paper proposes a JPEG2000 compression method based on wavelet transform, which can well overcome the
‘‘square” effect caused by DCT transform in the JPEG.
Finally, compare the JPEG2000 of this paper with the compression effect of JPEG standard and JPEG2000 standard respectively.
A study on various techniques of image compression is proposed to compress the image. The rest of the paper is organized as follows. Section II describes related work done in this area. Section III explain the various image compression methodologies. Section IV explain the various modules used in existing system and shows the observations of previous experiments and finally Section V yields the conclusion.
2. RELATED WORK
Generally compression methods can be categorized into two classes as lossy and lossless methods. Lossy compression can achieve high compression ratios (CR) compared to lossless compression methods. Several lossy methods which focus mostly on compression ratios without much consideration for image quality have been published Son et.al [19, 20]. A lossy hyper spectral image compression scheme, which is based on infra-band prediction and inter-band fractal encoding has been reported in Zhao et.al [21]. Chen Yaxiong et.al [22]
have proposed a lossy image compression scheme which combines principal component analysis and contoured transform to achieve high compression ratio along with better image quality. Zhao et.al [21] have proposed lossless and lossy delta compression schemes for time series data have proposed a prediction based lossless and lossy floating-point compression schemes for 2D and 3D grids in which the authors have used Lorenzo predictor and fast entropy encoding scheme. In have presented an image compression method, called prediction by partial approximate matching (PPAM) method which involves four basic steps, namely, preprocessing, prediction, context modelling, and arithmetic coding. A lossy compression scheme for medical images which makes use of a generic codec framework that supports JPEG 2000 with its volumetric extension (JP3D), and directional wavelet transforms.
They surveyed the recent developments in vector quantization codebook generation schemes which include enhanced method, neural network and genetic based algorithms with a context-based method to overcome the contextual vector quantization challenges.
In this paper, the authors have identified the regions of interest first, applied low compression to the identified regions with high compression applied to the background regions. Criterion et.al [23] have proposed a method based on the firefly algorithm to construct the code book using a vector quantization (VQ)-based lossy image compression method.
3. EXISTING METHODOLOGIES 3.1 JPEG compression method
Shyun et.al[1,2] have proposed JPEG is a compression standard by the ISO (International Organization for Standardization) and the CCITT (International Telegraph and Telephone Consultative
Committee) for color and monochrome multiple grayscale or continuous-tone still digital images [1].
There are several modes of JPEG, the most common of which is the sequential mode based on the DCT transform. In general, the JPEG compression algorithm operation can be divided into the following steps:
• Color change;
• MCU (Minimum Coded Unit), DU (Data Unit) and image sampling;
• DCT;
• Quantification;
• Zigzag scan;
• Run-length coding;
• Differential coding in the intermediate format;
• Huffman coding.
3.1.1 Color change
JPEG uses YCbCr color space. It is generally necessary to transform the color space of RBG. The RBG information in the original bitmap is converted to the Y representing brightness and Cb, Cr values representing chroma, which facilitates the following series of processing.
3.1.2 MCU(Minimum Coded Unit)
DU (Data Unit) and image sampling The Y component data is important, and the data of the CbCr component is relatively insignificant, so only a portion of CbCr may be taken to increase the compression ratio.
Currently, software that supports the JPEG format usually provides two sampling methods, YbCC4rn and YCbCr422 [9], meaning the data sampling ratio of the three components of YCbCr. Taking into account the factors of image quality, the JPEG standard specifies the minimum coding unit MCU. When the JPEG image is encoded and decoded, the smallest data block processed is an 8s data block, that is, a DU.
3.1.3 DCT(Discrete Cosine Transform)
JPGE is a two-dimensional discrete cosine transforms algorithm using an 8x8 sub-block [10]. The algorithm first divides the original image sequentially into a series of 8x 8 sub-blocks. In an 8x8image block, pixel values generally change more gently, so the image has a lower spatial frequency. Then discrete cosine transform is performed on the image block, so that the energy of the image block can be concentrated on a few coefficients in the upper left corner and the absolute value of these coefficients is very small. This is conducive to the subsequent compression process.
3.1.4 Quantification
8 x 8 image blocks after DCT transform, the low- frequency components are concentrated in the upper left corner, high-frequency components in the lower right corner. Quantification is to discard the information that has little effect on the visual effect under the premise of maintaining a certain quality. Linear uniform quantizer is used in JPGE standard. The quantification process is to divide 64 DCT coefficients by quantification step size and rounding. The frequency component is kept and the high frequency component is suppressed by
quantification processing. That is to say, the compression ratio can be further improved by using fine quantification for Y and coarse quantification for CbCr.
In decoding, inverse quantification is used, that is, the value to be processed is multiplied by the corresponding position value of the corresponding quantification table.
3.1.5 Zigzag scan
In order to ensure that low-frequency components appear first, high-frequency components appear afterwards to increase the number of continuous ‘‘0” in the run length, and the AC (Alternating Current) coefficient of the other 63 elements of 8 8 except the DC coefficient F (0,0), the ‘‘Zigzag” (219-Zag) arrangement method is used, and then run-length encoding is performed.
3.1.6 Run-length coding
The principle of run-length coding: The neighboring pixels with the same color value in a row are replaced with a count value and the color value. When the data is quantized, a large number of generated ‘‘0” can describe their length with only one data.
3.1.7 Differential coding in the intermediate format
Since the DC coefficients of the two adjacent 8x8 blocks are very small, differential coding DCPM (Differential predictive coding modulation) is used to increase the compression ratio.
3.1.8 Huffman coding
After getting the middle format, the number of parentheses in the example above is encoded by Huffman. Figs. 1 and 2 show the core contents of the processing steps of the encoder and decoder based on DCT in JPEG [11]. In the encoding process, the source image data is divided into 8x8 blocks. The forward DCT transforms each block into 64 DCT coefficients.
The amplitude of the spatial frequency transform coefficients is mostly zero or tends to zero. Thus it is possible to compress data. The forward DCT formula is [12]:
After outputting from the forward DCT, the quantizer quantizes the coefficient values according to the quantification table. Its purpose is to determine the step size Q(u, v) of the quantizer according to the quality of the desired image, and to represent the DCT coefficient F(u,v) with the corresponding precision to achieve further compression. Its quantification formula is [13]:
The decoding process is the reverse of the encoding process. The entropy decoder performs Huffman or arithmetic decoding. The inverse quantification process uses the approximate value obtained from the decoded data as the input of the inverse DCT. The inverse DCT transforms 64 coefficients by inverse transformation to reconstruct a 64 point output image.
4. JPEG2000 compression method
JPGE2000 is a new image compression standard Son et.al [19], whose goal is to allow the use of different image models (such as client/ server, real-time transmission and bandwidth resources, and so on) in a unified integrated system, the static images of different types (such as binary, grayscale, and so on) having different characteristics (such as natural images, medical images, remote sensing images, and so on) are compressed. Because JPEG2000 uses advanced encoding technology Lee et.al [20], JPEG2000 can facilitate progressive transmission, JPEG2000 supports both lossy and lossless compression has good low bit rate compression performance, and watermarking, marking, twisting and encryption can be used to achieve image security protection. The biggest difference between the JPEG2000 and the JPEG standards proposed in this paper is that it abandons the block coding method based on DCT (Discrete Cosine Transform) used in JPEG, and adopts a multiresolution coding method based on wavelet transform. The basic idea of the image coding method based on wavelet transform Zhao et.al [21] is to decompose the image into a low frequency sub-graph, a high frequency sub- graph in a horizontal direction, a high frequency sub- graph in a vertical direction and a high frequency sub- graph in the direction of diagonal lines by using the Mallat algorithm. After wavelet transform, each level of wavelet decomposition of the image data always divides the upper-level low frequency data into finer frequency bands. This method not only can obtain better compression effect, but also can overcome the
‘‘square” effect produced by DCT transformation in the JPEG
.
Fig. 4. The decoding process based on wavelet transform.
The JPEG2000 image encoding and decoding flow chart proposed in this paper is shown in Figs. 3 and 4.
Before the compression is performed, the source image data needs to be divided into tile rectangular units, and each tile is Criterion et.al [23] considered as a small source image. The specific encoding process steps are:
4.1 DC(Direct) level shift
The purpose of the DC level shift is to subtract these unsigned component sample values.
4.2 Component transformation
JPEG2000 encoding provides two kinds of component transformation: reversible component transformation and irreversible component transformation. The reversible component transformation can be used for lossless compression and lossy compression. The irreversible component transformation is only used for lossy compression. The reversible component transformation and irreversible component transformation formula are formula (3) and formula (4), respectively [22]:
where R, G, B represent three color components, Yr, Ur, Vr represent three color components after the transformation.
4.3 Wavelet Transform
A tile may consist of multiple components. Each component becomes a tile-component. Discrete wavelet transform is performed in units of tile-component. The number of decomposition stages depends on the specific application. The Mallat tower wavelet decomposition is performed for each tile. In the wavelet decomposition, a lifting wavelet transform fast
algorithm can be used. The use of a lifting wavelet transform is faster, requires less computational complexity, and requires less storage space.
4.4 Quantification
After each tile-component is decomposed by N-level wavelet, (3N+l) subbands are obtained. Each subband is quantized using different quantification steps, and the quantized wavelet coefficients are represented by sign and amplitude values.
4.5 Entropy coding
Entropy coding is divided into two steps: embedded block coding and hierarchical organization embedded block bit stream. The quantized sub-band is divided into small code blocks, and the coded blocks are used as the units for embedded coding. Then the block bit stream is encoded to calculate the cut-off point of the block bit stream on each layer. All block bit streams are organized according to cut-off points to form compressed code streams with different quality levels.
The code stream is hierarchically organized, each layer contains certain quality information, and on the basis of the previous layer, the image quality is improved. In this way, when browsing an image on the network, the first layer may be transmitted first, the user may be given a coarser image, and then the second layer may be transmitted, and the image quality may be improved on the basis of the first layer. With such transmission layer by layer, different quality reconstructed images can be obtained. The compressed code stream of PJEG2000 bit stream is formed. In order to be suitable for image exchange, JPEG200O specifies the format for storing compressed bit stream and decoding required parameters. The compressed code stream is organized in packets to form the final code stream.
Fig. 5 Effect of different compression ratios of the original image through JPEG2000.
Fig. 6 Effect of image compression using JPEG and JPEG2000 with different compression ratios.
5. CONCLUSION
This paper presents the details of the effective lossy and lossless image compression based on Huffman encoding process. In this paper various techniques for the image compression and lossy and lossless have studied and explained. The study shows the current image data compression technology cannot longer meet the needs of a wide variety of network multimedia applications. Among the various methods, JPEG2000 compression standard methods are giving more performance than the other methods. JPEG2000 is the most frequent standard. JPEG2000 can facilitate progressive transmission, JPEG2000 supports both lossy and lossless compression has good low bit rate compression performance. It has been widely used in image compression on the network. From this comparative study of JPEG and JPEG2000, the effective JPEG2000 standard produces the perfect reconstructed image as in the original image than the JPEG.
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