View of REVIEW OF VIDEO COMPRESSION TECHNIQUES BASED ON FRACTAL TRANSFORM FUNCTION AND SWARM INTELLIGENCE

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Vol.04,Special Issue 07, (RAISMR-2019) November 2019, Available Online: www.ajeee.co.in/index.php/AJEEE

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REVIEW OF VIDEO COMPRESSION TECHNIQUES BASED ON FRACTAL TRANSFORM FUNCTION AND SWARM INTELLIGENCE

Dr. Shraddha Pandit

Department of Computer Science & Engineering, Netaji Subhas Institute of Technology, Bihta, Patna

Dr. Piyush Kumar Shukla

Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal, Madhya Pradesh, India

Dr. Akhilesh Tiwari

Department of Computer Science & Engineering and IT, Madhav Institute of Technology &

Science, Gwalior, Madhya Pradesh, India

Abstract: Now a day many researchers are working on compression of videos and images using different approaches. There are different ways of performing such compression as fractal compression, wavelet transform, compressive sensing, contractive transformation and other way of compression is proposed. A way of doing such is working with high frequency component of multimedia data. One of the most recent is fractal transformation which follows the block symmetry and archives high compression ratio. Still there are limitations such as working with speed and its cost while performing the proper encoding and decoding using fractal compression. Swarm optimization and other related algorithms make it usable along with fractal compression function. In this paper, we review multiple algorithms in the field of fractal-based video compression and swarm intelligence for problems of optimization.

Keywords: Data processing; Decoding; Encoding; Frequency; PSNR; RMS; Swarm Optimization.

1. INTRODUCTION

With the increasing demand for images, videos, computer animations and multimedia technology, data compression is a serious topic concerning the cost of storage space and bandwidth. Data Compression [1] is an area of research, where the multimedia plays a vital role which needed to store and access over multiple data access platform. Different data compression transform functions are involved which leads in achieving better compression ratio [3]. Compression contains number of mapping, finding suitable pixel which can be minimize, finding the similarity pixel and further reduction of data without losing its impact on overall data. The concept of compression is first given by author Michael Barnsley [36] in 1987 for the development of fractal coding. The Concept of fractal encoding consists of transmission of image pixel which can minimize the data and keep it close to the compressed pixel. Brnsley discussed about the partitioning scheme using range block and then finding domain blocks which maps data of range block. A mapping with IFS iterating function scheme is also first given by Barnsley. The distance over the encoding measure is performed using mean squared error (MSE) value.

The Major classification can be discussed over the fractal compression such as range block-based [27] partitioning over the data, understanding the blocks which can be minimized, function understanding which can apply over data for transformation, finally presenting of data with the given matrix form and computing the loss, PSNR obtained after this process. The approaches of partitioning are also derived using multiple block base partitioning such as fixed size block, polygon block, horizontal vertical block, irregular region block, quad tree block and overlapping blocks. While fractal compression mainly aims to lead non-overlapping block with fast processing over multimedia data [4]. The disadvantage of fractal compression is large encoding time because of exhaustive search time. Therefore, decreasing the large encoding time is the main area of research. One way of decreasing the encoding time is by using stochastic optimization techniques using swarm intelligence.

In the given table 1 below, the comparison of video compression standards is shown.

This table shows the different standards and for the multimedia data compression. The main objectives to use the video compression techniques are used to convey multimedia transmissions, video streams contain a huge amount of data that requires a large bandwidth and subsequent storage space. As a result of the huge bandwidth and storage

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requirements, digital video is compressed in order to reduce its storage or transmitting capacity.

Table1.Comparison of Video Compression Standards [22]

Standard Applications Bit Rate

Motion-JPEG Still Image Compression Variable MPEG-2000 Improved Still Image Compression Variable MPEG-1 Video on Digital Storage Media 1.5 mb/s

MPEG-4 Object Based Coding Variable

H.261 Video Conferencing Over ISDN Px64kb/s

H.263 Video Telephony Over PSTN 33.6kb/s

H.264 Improved Video Compression 10’s to 100’s kb/s

The above table shows the standards came from time to time for multimedia video technology. Quality of data is increasing with the effect of time where the video compression contains encoder, compression over the available bit stream and finally decoder.

Remaining paper minutiae in the parts or sections. In section 2 discuss the Fractal Video Compression which discuss about the process of transformation. In section 3 discuss Swarm Intelligence which is used for speeding up the fractal processing. Section 4 discusses the related work of video compression based on fractal and Swarm Intelligence in discussion form. In section 5 confer the performance analysis of video compression using diverse approaches and finally discuss Conclusion & Future Scope.

2. FRACTAL TRANSFORM

Michael Barnsley [36] has given a concept of conversion image into compress manner given lossy image compression [4,8]. The fractal transform function used the property of similarity [6,12]. The derived approach includes the fractal compression and decompression where its block and non-overlapping blocks make it important than other compression techniques [5].

Fractal compression process is independent of the resolution of the original image [20]. The output graphics will look like the original at any resolution, since the compressor has found an IFS whose attractor replicates the original one (i.e. a set of equations describing the original image). Of course, the process takes a lot of work, especially during the search for the suitable range regions. But once the compression is done, the fractal image file can be decompressed very quickly. Thus, fractal image compression is asymmetrical. The practical implementations of a fractal compressor offer different levels of compression. The lower levels have more relaxed search criteria to cut down processing time, but with the loss of more details. The higher levels give very good details, but take a long time to process each image.

Figure 1: Video encoding and decoding process of fractal transform

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Figure 1 above, represents the video encoding and decoding process on a group of frames.

3. SWARM INTELLIGENCE

A minimization of process can be achieve using an advance artificial neural network approach and hence swarm intelligence play the role. Swarm intelligence is a mathematical model using which the boosting over fractal compression is applied. The swarm intelligence gives variety of algorithms which are broadly categorized into two kinds such as animal based and insect-based algorithms. Shu Chuan Chu et.al [32] has proposed an outline of many Swarm Intelligence algorithms. Particle swarm optimization (PSO) was originally introduced by Kennedy and Eberhart in 1995. It is based on the social behaviors of bird flocking or fish schooling. But last years PSO has been turned out to be another powerful tool besides other evolutionary algorithms such as genetic algorithm (GAs). Particle swarm optimization, ant colony optimization, Bee colony optimization and many more meta- heuristic functions for the process of optimization. Some authors used multi-objective particle swarm optimization of video encoding techniques along with wavelet transform function. Here discusses PSO-particle swarm optimization which works with the multiple space surfaces and find an optimal solution using its minimization process. PSO is a heuristic search method that implements the movements of a flock of birds which aim to search to get food. The relative simplicity of PSO and the fact that is a population-based technique have made it a natural candidate to be extended for multi-objective optimization [32].

Figure 2: The process block of particle swarm optimization [35]

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3.1 Important factors of Particle Swarm Optimization

There are two main concepts in the PSO algorithm: velocity and coordinate for each particle.

Each particle has a coordinate and an initial velocity in a solution space. As the algorithm progresses, the particles converge towards the best solution coordinates. Since PSO is quite simple to implement, it requires less memory and has no operator. Due to this simplicity, PSO is also a fast algorithm.

4. RELATED WORK

This section discusses about the overview of work performed by previous authors. The study discussed about the different transformation function used for fractal encoding. Video compression technique such as DCT transformation, Wavelet transmission and other fractal transmission compression functions are discussed as overview of given authors study.

Hongqin Shi, FangliangLv and Yiqin Cao Et al. [1]: This examination introduces another shading watermark implanting system with course, in view of non-covering Singular Value Decomposition (SVD) for stowing away essential data in images.

Shweta Pandey and Megha Seth Et al. [2]: In this author explained about a strategy of Fractal image pressure is finished by quadtree deterioration. In Quadtree strategy the piece estimate changes as indicated by the components of the image and isolated into various squares.

Jaya Shrivastava and Neelesh Shrivastava Et al. [3]: They utilize altered wavelet change with high lift channel (WT-HBF) in spatial area. The exploratory consequences of their approach are considerably more effective in lessening the measure of image than the current methods.

Yong Xu, Delei Liu, Yuhui Quan and Patrick Le Callet Et al. [4]: In this examination, multifractal examination is changed in accordance with reduced reference picture quality evaluation (RR-IQA). A tale RR-QA approach is discussed, which estimates the refinement of spatial course of action between the reference picture and the distorted picture the extent that spatial typicality estimated by fractal estimation.

Y. Sanchez, T. Schier, C. Hellge, T. Wiegand and D. Hong Et al. [5]: They shows that framework resources are even more efﬁciently used and how the beneﬁts of the traditional methodologies can even be inspired by accepting the Scalable Video Coding (SVC) as the video codec for adaptable low concede spouting over HTTP.

Chaurasia.V. and Somkuwar. [6]: The examination diagrams the noteworthy picture weight techniques spreading over across over lossy and lossless picture weight frameworks and clears up how the JPEG and JPEG2000 picture weight methods are undeniable from one another.

Thomas Schier, Miska M. Hannuksela, Ye-Kui Wang and Stephan Wenger Et al. [7]:

This investigation gives a short audit of the irregular state accentuation of HEVC and the association with the Advanced Video Coding standard (H.264/AVC). A zone on HEVC botch quality shuts the HEVC audit.

Dr. Fadhil Salman Abed and Iraq-Diyala-Jalawla Et al. [8]: In this exploration, another concealing strategy is talked about in light of encoding the data by utilizing RSA cryptosystem and concealing the figure information (cipher text) by utilizing examined fractal image compression (FIC).

Ryan Rey M. Daga and John Paul T. Yusiong Et al. [9]: Image compression systems are vital and helpful in information stockpiling and image transmission through the Internet. Investigation comes about demonstrate that it is possible to utilize the agreement scan calculation as a calculation for image pressure. The HSA-based image pressure strategy could pack hued and grayscale images with insignificant visual data misfortune.

Zhou. Y. M., Zhang. C Et al. [10]: This examination speaks to the different recognition and division procedures of X-Ray bone break Detection using Edge distinguishing proof counts. Edge acknowledgment is associated with find the break in a bone in the body. Further K. Sharmila and K. Kuppusamy Et al. in [11] perform examination, a blend of Discrete Cosine Transform and fractal with quadtree methodology and Run Length Encoding is discussed to pack the picture. Execution result shows that the picture is compacted suitably using the discussed work. While K. Hariharkrishnan and Dan Schonfeld in paper [12] says that differing review ask about exists related to fractal coding, this investigation is unmistakable in many senses. One can develop the new shape

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structure for development estimation and adjust the present square planning development estimation with automata coding to explore the fractal weight system with specific focus on decreasing the encoding time and achieving better picture/video entertainment quality.

G.Sandhiya, M.Rajkumar and S.G.Vishnu PrasadEt al. [13]: The Discrete wavelet change (DWT) has increased across the board acknowledgment in flag preparing and image pressure. Due to their characteristic multi-determination nature, wavelet-coding plans are particularly reasonable for applications where adaptability and fair corruption are essential.

The vast majority of the DWT models use pipelining and combinational rationale configuration to decrease power and chip estimate.

Dr.S.Sukumaran and MrT.Velumani Et al. [14]: They analyzed the still image procedures of significant intrigue prescient and change coding, vector quantization, and sub-band and wavelet multiresolution approaches. Finally, comments are offered in respect without limits appropriateness of coding measures, of less especially investigated estimations, and the general position of the picture and video weight strategies in the rapidly making field of visual information game plan.

Saad Al-Azawi, Said Boussakta and Alex Yakovlev Et al. [15]: This examination discussed low and direct multifaceted nature estimations for shading picture weight. Two figuring’s will be shown; the first is control based flexible quantization coding, while the second is a mix of discrete wavelet changes and the power based adaptable quantization coding computation.

K. Raja Kumari and C. Nalini Et al. [16]: In this paper author proposed both Encoding procedure and image pressure and experimentation is performed. To think about the outcomes, the mean square error, motion to-commotion proportion and encoding time rules and pressure proportion (bit per pixel) were utilized. The straightforwardness to acquire compacted image and removed shapes with acknowledged level of the remaking is the fundamental favorable position of the discrete wavelength change calculations.

A. Alarabeyyat, S. Al-Hashemi, T. Khdour, M. HjoujBtoush, S. Bani-Ahmad and R.

Al-Hashemi Et al. [17]: In this work, enhancing the viability of lossless picture compression by enhancing the BCH and Lempel-Ziv-Welch was discussed. They gave an outline of different existing coding norms lossless picture compression systems. They have also discussed about a high proficient algorithm which is actualized utilizing the BCH coding approach. A definitive objective is to give a moderately decent compression ratio and keep the time and space multifaceted nature least. The trials wereS carried on accumulation of dataset of 20 test pictures. The outcomes were assessed by utilizing compression ratio and bits per pixel. The test comes about demonstrate that the examined algorithm enhances the compression of pictures contrasting thought about and the RLE, Huffman and Lempel-Ziv- Welch algorithms. Author Miska M. Hannuksela, Dmytro Rusanovskyy, Wenyi Su, Lulu Chen, Ri Li, PaymanAﬂaki, Deyan Lan, Michal Joachimiak, Houqiang Li and MoncefGabbouj Et al. in paper [18] given the concept which displays a multi-see video-in addition to profundity coding plan, which is perfect with the propelled video coding (H.264/AVC) standard and its multi-see video coding (MVC) augmentation.

D. Venkatasekhar, P.Aruna and B.Parthiban Et al. [19]: This paper spoke about PSO based strategy which is utilized for the mean square error (MSE) in view of the halting standard between go block and memory block. Dihedral transform, just four transforms for every area piece are viewed as in order to decrease the encoding time. A test come about on encoding time of the examined strategy is quicker than that of the ordinary techniques on great picture quality. In this paper, molecule swarm enhancement technique is embraced with grouping and Dihedral transform so as to accelerate the fractal picture encoder. By utilizing particle swarm optimization (PSO) based technique for fractal coding can reduce CPU time, PSNR and MSE Values and delivers better compression ratio at adequate quality, when contrasting and existing full pursuit, wavelet characterization and SGA strategies.

Shiping Zhu, Liyun Li, Juqiang Chen and Kamel Belloulata Et al. [20]: They examined a novel fractal video game plans codec with customized locale-based convenience.

The results exhibit that, everything considered, the discussed customized region-based fractal video progressions coding system can save weight time 48.97% and bitrate 52.02%

with some picture quality corruption in examination with H.264. whereas Author Lei Yu, Houqiang Li and Weiping Li Et al. in paper [21] discussed about broadcasting/multicasting the recordings with CIF and QCIF resolutions and also spokes about WSVC beats Soft Cast normal and outflanks SVC+HM.

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Omaima N. Ahmad AL-Allaf Et al. [22]: The exploratory outcomes demonstrated that diminishing the GA populace estimate and expanding number of specialists utilized for the three parallel figuring philosophies can reduce the weight time. Best results procured from executing parallel techniques with 6 experts and 150populace measures. Genetic rule generate and population calculation help in finding best fitness value over the block obtained and hence finding the rules using which the best compression with high quality can be determined. Author Kamel Belloulata, Amina Belalia and Shiping Zhu Et al. in [23]

discussed powerful macroblock segment conspire rather than traditional quadtree segment plot; in this manner diminishing the piece looking system. More multidimensional paradigm is discussed at this level of compression.

Taha mohammed Hasan and Xingqian Wu Et al. [24]: In this investigation an Adaptive Fractal Image Compression (AFIC) estimation is inspected to reduce the long time of the Fractal Image Compression (FIC), where the polygon structure separate based pressure helps in discovering lower in calculation time and also better pressure proportion.

While Mohammad Reza Faraji and Xiaojun Qi Et al. in [25] discussed about the fractal examination (FA) to convey logarithmic fractal estimation (LFD) picture which is edification invariant. Picture weight-based pressure can be connected over to locate the best fit over picture information pressure.

Junshe Wan and Linru You Et al. [26]: In this paper, basing on the segment, author has proposed GA to improve the quality of video compress through adjust the fixed parameter to suitable value in fractal compress algorithm. For improving speed, this paper described PSO to speed up the fractal video compress. Experimental results show that the PSO could better match the requirement of searching the best domain comparing GA method on the premise of guaranteeing the decoded image’s quality.

M.Thamarai and R.Shanmugalakshmi Et al. [27]: In this study video coding using directional transform DDWT is considered and also multi objective particle swarm optimization (MOPSO) has proposed. In this techniques objective function of Entropy, computation time and mean square error are considered. Author Gohar Vahdati, Mahdi Yaghoobi and Mohammad Reza Et al. [28]: In this experimentation a spatial correlation chaotic particle swarm optimization based on characteristics of fractal and partitioned iterated function system is proposed. Author used spatial correlation in images for both range and domain pool to exploit local optima and adopted chaotic PSO to explore the global optima. Result shows that proposed algorithm saved encoding time and obtained high compression ratio.

A.Muruganandham and Dr. R. S. D.Wahida Banu Et al.[29] : In this investigation author has proposed fast fractal encoding system using particle swarm optimization to reduce the encoding time. Here MSE based optimization technique is used which finds MSE between domain block specified by the particle positions and given range block, this fast fractal encoding system speedup the fractal encoder and preserved the image quality for medical images. While author Chun-Chieh Teseng, Jer-Guang Hsieh and Jyh-Horng Jeng Et al.[30] has proposed visual based particle swarm optimization with k restriction a nd intuitive move has been proposed for fractal image compression, In this author implemented three types of PSO, which are traditional PSO,PSO with k restriction and PSO with both k restriction and intuitive move, Experimental results show that searching space is reduce which is 125 times faster with only 0,.89 dB decay of image quality in comparison to the full search method.

5. RESULT OVERVIEW

This part analyzed results of video compression techniques using different methods; all methods used transform function in different ways for the compression process. The performance parameter used PSNR, compression ratio, encoding time, speedup factor, compression and mean square error. Table 1 represents analyzed results of video compression using different methods.

6. DISCUSSION

As per the literature observation and research study performed, there are many algorithms proposed for the data processing. Multimedia data is involved in many places such as social media, smartphone, application storage etc. Thus, an efficient way of storage and access them is always required which provide an effective quality of services to the end user.

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Finding the video compression using the fractal transform function along with fast swarm intelligence is one of the latest solutions observed.

7. CONCLUSION & FUTURE WORK

Multimedia data processing and the compression as an important factor while performing its storage as well as access over different domain. Data compression uses encoding and decoding, doing such needed appropriate function of processing it. Fractal compression needed proper transformation function using which loss less data can be achieved.

Different approaches such as wavelet transform, compressive sensing, signal analysis, discrete transform and other fractal transformation functions are discussed in the literature. The approaches other than fractal transform are limited which perform overlapping of blocks involved in it. Fractal transform function involves non-overlapping blocks which are efficient and time efficient as compared to other overlapping approaches.

Still the process is needed to optimize using which a fast encoding can be performed, thus efficient swarm intelligence is built over fractal transform function. Finding a better way of compression using fractal transform along with fast search using fractal transformation is analyzed. The searching and matching of blocks in transform function are an issue for the encoding process. Encoding scheme such as tree encoding, multi-encoding and run-length encoding processes are also proposed in the literature work. An extension of such work is considered as future work where dual heuristic based searching techniques for video compression is need to be performed. A heuristic based search is going to compute less computation time as well as cost, as it’s efficient while processing the multiple data simultaneously.

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