A Voyage on Compound Image Compression Techniques
Dr L C Manikandan1 and Prachod P 2
1Professor & HoD, CSE, Musaliar College of Engineering & Technology, Pathanamthitta, Kerala, India. E-mail: [email protected]
2Asst.Professor, CSE, Musaliar College of Engineering & Technology, Pathanamthitta, Kerala, India. E-mail: [email protected]
Abstract
The demand for storage capacity and bandwidth of communication channel exceeds the availability, though there is much technological advancement. The role of compound images is inevitable in web based applications. The compound images comprise of text and picture and they have anisotropic features, hence the conventional compression algorithms are not enough to yield good results. This paper presents a literature analysis in various compound image compression techniques.
Keywords: Segmentation, Compression, Compound image, JPEG, DCT, DWT
1. Introduction
Digital Image compression is the technique of reducing the amount of data needed to represent a digital image, thereby removing the redundant data. The fundamental goal of image compression is to diminish the quantity of bits required to store and transmit pictures with no loss of vital data. The requirement for compression is important due to the headway in innovation in information transmission at higher band width and capacity limit. The compression algorithms are principally grouped in to two types; lossy and lossless methods [1]. The lossy compression techniques are broadly utilized for natural scenic images where the loss of any data is adequate [2]. The lossless compression models are used in those situations where the data is essential. The compound images are the blend of content, designs and common picture and it assumes a fundamental part in numerous applications [3]. In cloud computing, the compound pictures are utilized for different purposes and subsequently the compression and transmission of these pictures picks up a great deal of significance. Section 2 and 3 describes the literature analysis of segmentation and compression models for compound images.
2. Compound Image Segmentation Models and Compression Models
In compound image compression, the foremost step is the segmentation of components from the compound images and compresses them individual ly by suitable coders. The segmentation in compound image compression is based on three approaches (i) Object based, (ii) Layer based and (iii) Block based techniques [4][5]. In object based approach, the image is divided into regions, where each region corresponds to an object in the image with well-defined boundary. The object based approach is rarely used because of its complexity. In Layer Based approach, the image is splitted into rectangular layers and it generally follows the 3 -layer Mixed Raster Content (MRC) model.
Figure 1. Principle of Mixed Raster Content (a) Original image (b) Background (BG) layer (c) Foreground (FG) layer (d) Mask layer
The MRC comprises of foreground, mask layer and background layer. The foreground layer comprises of text and the background layer comprises of picture elements. The binary mask layer specifies whether the pixels are in the background or foreground layer [6] [7]. Consider a compound image comprising of Lena picture with text as shown in Figure 1. The background layer contains the image and the foreground layer contains the text. The mask layer is a binary layer and each layer is compressed separately by the encoder based on the characteristics. The segmentation module depicted in Figure 2 splits the compound image into three layers/blocks and each layer/block is compressed by suitable algorithm. The inverse of the above process is done in the receiver side and is depicted in Figure 3. The data filling (plane filling) is done before processing each layer as a pre-processing stage to avoid the halo effects and is depicted in Figure 4. An artificial neural network algorithm has wide variety of applications and back propagation neural network can be used for the segmentation of layers in MRC model. In block ba sed approach, the image is classified into rectangular blocks where each block represents an object in the image with well-defined boundaries. Based on the statistical characteristics of each block, they are compressed using different techniques [8]. The blocks are generated from compound image commonly by histogram gradient, colour thresholding and discrete cosine transform (DCT) based techniques.
Figure 2. Block diagram in transmitter side
The MRC is the international standard for compound image comp ression and the block diagram of the MRC system is depicted in Figure 2 and Figure 3. Each layer in MRC model is compressed by a specific algorithm to achieve better compression ratio.
Figure 3. Block diagram in the receiver side
Figure 4. Data filling in MRC model
3. Comparative Study
The comparative study of compound image compression techniques based on layer based model is shown in Table 1 and Compound Image Compression based on block based model is shown in Table 2.
Table 1. Compound image compression techniques based on layer based model
Reference Paper Details
Algorithms Used
Comments Performance Analysis
Ricardo L.
de Queiroz et al [9]
JPEG and MMR compressor
Optimized block thresholding is used for segmentation of layers in MRC model.
The foreground and background non-binary planes are compressed by JPEG coder and binary mask plane is compressed by MMR coder.
The proposed compression model was found to be efficient for image with more graphics.
Performance is better when compared with single coder such as JPEG2000 and JBIG2 in terms of PSNR
Roumen Kountchev [10]
Inverse Difference Pyramid (IDP) image
decomposition
Used for the compression of compound still images with text and graphics.
It recognizes ROI as
Hybrid of IDP and HARL compression model efficiency outperforms in compression ratio
and histogram- adaptive run- length coding (HARL)
compression model
text and pictures based on histogram analysis
with less
computational complexity.
when compared to IDP and HARL compression
algorithm individually.
Weijia Zhu [11]
HEVC Intra coding
framework
Compound image compression is based on base color and
index map
representation.
Template and
directional prediction is used for structural mapping.
The proposed method has better performance than HM6.0, BCIM and DIPM compression model.
Andriy Gelman [12]
3D Discrete Wavelet
Transform (DWT) coder
The layers are compressed by 1D DWT across the view point dimension and shape adaptive 2D DWT across the spatial dimensions.
The proposed compression model yields better results than H.264/AVC in terms of PSNR.
Shuhui Wang [13]
Unified LZ and Hybrid Coding (ULHC)
Compound images with text and pictures are coded in macroblock using gzip and H.264 with rate distortion
optimization.
The PSNR is high for the ULHC than gzip and H.264 compression model.
S. Ebenezer Juliet [14]
JPEG 2000 and JBIG2
compressor
The foreground and background layer is compressed by JPEG 2000 and mask layer is compressed by JBIG2 compressor.
The proposed compression model yields better result than JPEG and JPEG 2000.
D.Maheswari [15]
Token based coder, JPEG compressor
and JBIG
compressor
The text layer is compressed by token based coder, graphics layer is compressed by JPEG coder and mask layer is compressed by JBIG coder.
The proposed compression model yields better results than block based model in terms of compression ratio and PSNR
Table 2. Compound Image Compression based on block based model
Reference Paper Details
Algorithms Used
Comments Performance
Analysis
Tony Lin et al [16]
Shape Primitive Extraction and Coding
(SPEC)
The color thresholding is used to segment the compound image into text/graphics pixels and pictorial pixels. The text/graphics pixels are compressed using lossless algorithm and pictorial pixels are compressed using lossy JPEG algorithm.
The SPEC algorithm is compared with the JPEG, JPEG 2000, DjVu and LZW algorithms. DjVu and SPEC performs much better than JPEG, JPEG 2000, however SPEC has lower complexity and high compression quality than DjVu.
Wenpeng Ding et al [17]
Arithmetic Coder, wavelet coder and JPEG coder
The gradient histogram technique is used for block classification and the blocks are grouped into four classes (smooth, text, hybrid and picture).
The smooth blocks (dominated by one colour) and text blocks are compressed by arithmetic coder. The hybrid blocks comprising of text and picture are compressed by haar wavelet based coder.
The picture blocks are compressed by JPEG coder.
The proposed model outperforms JPEG and DjVu coder in terms of PSNR .The ringing effects in the text blocks are also eliminated.
Zhaotai Pan [18]
Hybrid JPEG/PNG codec
Browser-friendly hybrid JPEG/PNG codec is proposed for compound images. JPEG is used to encode the pictorial blocks and PNG is used for compression of text blocks. Pixel-domain quantization mechanism before the PNG coder is used to remove less sensitive information.
Hybrid JPEG/PNG codec performance is
better when
compared with JPEG, JPEG2000 and DjVu in terms of visual quality.
Cuiling Lan et al [19]
High efficiency video coding (HEVC), Residual Scalar Quantization (RSQ), Base Color and Index Map (BCIM) and dictionary based lossless compression (LZMA) model
High efficiency video coding (HEVC) is used for the compression of natural image contents and text/graphics contents are compressed by LZMA, RSQ and BCIM model.
The proposed
compression model (HEVC + RSQ + LZMA+BCIM) yields better results than HEVC+ RSQ, HEVC+BCIM and HEVC+LZMA model in terms of PSNR
Shuhui Wang et al [20]
LZ Dictionary- based
algorithm (enhanced gzip) and H.264 coder
The text/graphics macro blocks are compressed by lossless algorithm (enhanced gzip) and the picture macro blocks are compressed using H.264 coder.
The performance of proposed algorithm (modified gzip and H.264) is better than H.264 in terms of visual quality and modified gzip in terms of higher compression ratio.
Yongi Yang [21]
Spatially adaptive recovery algorithm
This algorithm is based on the theory of projections onto convex sets. It captures both local statistical properties and human perceptual characteristics for analysis.
The proposed method is better when compared with JPEG and projection based image decoding approach
Ricardo L.
de Queiroz [5]
Mixed Raster Content
(MRC) multilayer approach for compound image compression.
The compound image containing text and graphics uses MRC model along with optimised block thresholding for faster approximation of blocks.
The MRC performs better than JPEG with respect to PSNR.
Cuiling Lan et al [22]
HEVC and Lossy/
Lossless LZMA model
The natural image is compressed by HEVC and text/graphics contents are
compressed by
Lossy/Lossless LZMA model
The HEVC + lossless
LZMA model
outperforms the HEVC + lossy model; however
HEVC +
lossy/lossless LZMA model (unified
LZMA model)
performance
outperforms the
above two in terms of PSNR.
D.Maheswari [23] [24]
Hybrid Compression Model and
Wavelet compressor
The compound image is first subjected to block based approach and is splitted into pure picture block, pure text block, overlapping block, back ground and graphics block. The overlapping block comprising of text and picture is compressed by wavelet coder and layer based technique. In layer based model the text is compressed by token based coder, mask layer is compressed by JBIG coder and graphics layer is compressed by JPEG coder.
Hybrid model
comprises of layer and block based approach achieves better results than wavelet model. The hybrid model also yields better results than JPEG and DjVu compression model in terms of PSNR and compression ratio.
T.Nakayama, M. Konda [25]
Adaptive Resolution Vector Quantization (AR-VQ) technique and Systematic codebook design method
The compound XGA images was subjected to an improved vector quantization (AR-VQ) that comprises of three stages; Laplacian edge detector, resolution conversion to 8x8 pixel blocks for organization of block patterns, entropy coding
The AR-VQ
algorithm has good
PSNR and
compression ratio when compared with JPEG 2000 standard.
In [26] the compound image is lossless compressed by initially by segmenting text and graphics by shape primitives extraction and coding (SPEC) and the bit streams was encoded by LZW encoder. Shuhui proposed a macroblock based technique for compound image compression and video compression which encodes the bits by H.264 and gzip simultaneously and again the bit are encoded with Unified LZ and Hybrid Coding (ULFC) which improves the rate of distortion [27].
The descriptive analysis by A.N. Skodras gives the brief about features, functionalities, prons and corns, applications of the JPEG 2000 standard of the still images [28].
Wenpeng Ding proposed as technique which achieves a better gain compared with H.264/AVC intra coding which identifies text and image blocks and generates base colour for representation encoded with structure aware context based arithmetic coder [29]. Amis Said suggested a segmentation method for compound image compression which estimates the object boundaries which eliminates the localization and precision. The compression ratio is better when compared with gzip, baseline JPEG, JPEGLS [30].
Xi Qi proposed a multistep text extraction for compound image compression which preserves the text quality by distinguishing background and the text with different schemes [31]. The performance of H.264/AVC was evaluated with PSNR, MSE and Compression ratio by comparing the algorithm D-CIC, ZQ-CIC compound image intraframe prediction compression models [32]. D.Maheswari proposed a hybrid block and layer based segmentation and compression for compound images.
This modified method was evaluated with PSNR, Compression ratio and execution time [33].
The block classification based on the histogram improves the segmentation text, background, hybrid and image areas. The text, and background uses JPEG encoding, image uses Run length and wavelet encoding, hybrid uses H.264AVC and CABAC entropy encoding [34]. Priya Vasanth proposed a compression model to optimize the coefficients of Discrete wavelet transform and Harr wavelet with Oppositional based Grey Wolf Optimizer (OGWO) [35].
The Table 1 and Table 2 depicts the various layer based and block based compression techniques in compound images. The performance metrics widely used to analyse the efficiency of compression algorithm are PSNR and M SE. The mean square error (MSE) is calculated from the compressed image and input image. It determines the energy loss in the original image after compression. Smaller the values of MSE, the energy loss are minimum and better the performance of algorithm. The higher PSNR better the compression technique.
(1)
(2)
Where f (m, n) is the input image and f '(m, n) is the reconstructed image. The computation time is used to qualify the compression algorithm and it depends on the processor capacity. The compression ratio is also a widely used metric to evaluate the compression algorithm efficiency and it is defined as the ratio of output file size to the input file size in percentage. Apart from the classical performance metrics, the widely used other metrics are Normalized Cross correlation (NCC), Normalized Absolute Error (NAE), Laplacian Mean Square Error(LMSE) and Structural Content (SC). The value of NCC and SC should be closer to „1‟ and NAE, LMSE should be low for a good compression algorithm.
4. Conclusions
This paper describes the segmentation models in compound images prior to compression. The widely used compression models in compound images are based on layer based and block based approach. A literature analysis based on layer and block based models with different compression algorithms is done here. The outcome of this work will be guidance for the researchers who are working on compound image compression techniques to develop a novel algorithm for prolific result.
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Authors
Dr L C Manikandan is working as Professor and HoD in CSE at Musaliar College of Engineering and Technology, Pathanamthitta, Kerala, INDIA. He has received Ph.D. and M.Tech. Degree in Computer and Information Technology from Manonmaniam Sundaranar University, M.Sc., Degree in Computer Science from Bharathidasan University and B.Sc. Degree in Computer Science from Manonmaniam Sundaranar University. His main research interest includes Video Surveillance, Image Processing and Cloud Computing.
Prachod P is working as Assistant Professor at Musaliar College of Engineering and Technology, Pathanamthitta, Kerala , INDIA. He has received M.Tech. Degree in Computer Science and Engineering from SRM University, Tamilnadu and B.Tech.
Degree in Computer Science and Engineering from Mahathma Gandhi University, Kerala. His main research interest includes image processing.
