ABSTRACT: This paper describes a simple but effective predictive coding technique for color **image** **compression** using color quantization. In Android mobile devices, Memory management has become a major apprehension because it has significant impact on performance of system and battery life. Also it is important to efficiently use and manage the internal and external memory space available inside the mobile operating system. Efficient memory consumption plays an important role in better performance of the device. So it is essential to make a facility that helps in reducing memory consumption. The proposed Android **Image** **Compression** Tool compress the color **image** using **lossless** **Image** **Compression** **Algorithm** using predictive coding built on Color Quantization for Android Mobile Devices. The objective of **image** **compression** is to shrink the redundancy of the **image** and to store or transmit data in an efficient form. The experimental results reveal that the **algorithm** is effective and yields a better performance time when compared with other existing mobile applications. The key goal of such system is to decrease the storage quantity as much as possible and encoded **image** displayed in the output screen that will be similar to the original **image**. This proposed system will reduce the **image** size while achieving the best **image** quality with lesser data loss.

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In this scenario presents a **lossless** **image** **compression** and calculation. To achieve high **compression**, speed, operate a straight forecast, SPMO can execute all displaying strategy. Contrast tone and gray scale IM digital bit can improve up to 16-bits. This work results are particularly useful for high, small, medium dimensional **image** applications. The average comp speed on Intel Xeon 3.06GHz CPU is 47MB/s. For enormous pictures, the rate is over 60MB/s, i.e., the calculation needs under 50CPU cycles per byte of the **image**. Our calculation is prescient and versatile; it packs continuous tone grayscale pictures. The picture is handled in a raster-check request. Right off the beat, we perform expectation utilizing an indicator chose from a ﬁxed set of 9 direct fundamental indicators. Forecast mistakes are reordered to get likelihood appropriation expected by the information model and the entropy coder, and after that give way as a succession of residuum images. For encoding scheme images, we utilize a group of preﬁx codes dependent on the SPMO model. For quick and versatile displaying, we use a straightforward setting information model dependent on a model of the SPMO calculation [1] and the strategy for diminished model update recurrence [2]. The calculation was intended to be necessary and quick. We don't utilize techniques, for example, identification of smooth locales or tendency dropping. Decompression is an essential inversion of the comp procedure. To predict the power of a speciﬁc pixel X, we utilize quick straight indicators that utilization up to 3 neighboring pixels: the left-hand neighbour (A), the upper neighbor (B), and the upper-left neighbor (C). We utilize 8 indicators of the **Lossless** JPEG calculation [3], and one more unpredictable indicator Pred8, that restores a normal of Pred4 and Pred7.

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Compared to the conventional endoscopy system, the wire- less capsule endoscopy allows us to directly study the entire small intestine and does not require any sedation, anesthesia, or insuﬄation of the bowel. There is the only clinic device made by Israel in the world [1]. However, it can only work for less than eight hours (generally, it costs capsule about 10–48 h, typically 24 h, on moving from mouth to evacua- tion [1]) and the **image** frame rate is slow (2 frames/second), which results in the fact that there is not enough time for the capsule to check the large intestine, and some regions inter- esting to the doctors are often missed. By analyzing power consumption in capsule, it can be known that the power of transmitting **image** data occupies about 80% of the total power in capsule. In the digital wireless endoscopy capsule system designed by us, the power is supplied by two batter- ies and a CMOS **image** sensor is used [2]. In order to reduce the communication bandwidth and the transmitting power in the capsule, the **image** **compression** has to be applied. Al- though the CMOS sensors will bring some noises to the cap- tured images, it does not aﬀect the doctor’s diagnosis. Once the lossy **compression** is used, some information contained in the original images will be lost, which results in the error diagnosis. So a new low-complexity and high-quality **image** **compression** for digital **image** sensor with Bayer color filter arrays needs to be used [2]. Figure 1 shows the simplified

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Abstract — In modern communicative and networked computing, sharing and storing **image** data efficiently have been a great challenge. People all over the world are sharing, transmitting and storing millions of images every moment. Although, there have been significant development in storage device capacity enhancement sector, production of digital images is being increased too in that proportion. Consequently, the demand of handsome **image** **compression** algorithms is yet very high. Easy and less-time-consuming transmission of high quality digital images requires the **compression**-decompression (CODEC) technique to be as simple as possible and to be completely **lossless**. Keeping this demand into mind, researchers around the world are trying to innovate such a **compression** mechanism that can easily reach the goal specified. After a careful exploration of the existing **lossless** **image** **compression** methods, we present a computationally simple **lossless** **image** **compression** **algorithm** where the problem is viewed from a different angle- as the frequency distribution of a specific gray level over a predefined **image** block is locatable, omission of the most frequent pixel from the block helps achieve better **compression** in most of the cases. Introducing the proposed **algorithm** step by step, a detailed worked out example is illustrated. The performance of the proposed **algorithm** is then measured against some standard **image** **compression** parameters and comparative performances have been considered thereafter. It has been shown that our approach can achieve about 4.87% better **compression** ratio as compared to the existing **lossless** **image** **compression** schemes.

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In this paper, a modern **lossless** **image** **compression** **algorithm** using duplication free run-length coding (RLC) is proposed. To encode entropy rule-based generative coding method is proposed. Based on these rules variable length code words was generated and the resulting code words are utilized to encode **image**. The proposed method is the ﬁrst RLC **algorithm** that encodes the case of two consecutive pixels of the same intensity into a single codeword, hence gaining on **compression**. Also, the number of occurrence (i.e., run) that can be encoded in a single codeword is inﬁnite. When compared to the other methods this method gives better **compression** ratio.

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Then use LZW for **compression** and finally it use retinex **algorithm** on compressed **image** for improve **image** quality. Energy Aware **Lossless** Data **Compression** [4] illustrates various data **compression** methods for **lossless** and their performance on **image**. It can be estimate the **compression** ratio and also calculate the time for **compression** and decompression of **image** when using various **compression** algorithms. Simple Fast and Adaptive **Lossless** **Image** **Compression** **Algorithm** [5] explains **lossless** methods and proposes an **algorithm** named as an SFALIC.

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for subtraction are column adjacent whereas in row folding, the pixels are row adjacent. The pixel redundancies are rearranged in a tile format and Arithmetic encoding technique is applied which results the compressed **image** at the end before transmitting the data. The idea is to reduce the **image** size iteratively in terms of dimensions rows or columns by 2. At the decoder, Arithmetic decoding is applied followed by data unfolding which is similar to data folding. Difference data thus obtained through all iterations is stored in the tile format. Data Folding is an **algorithm** for **lossless** **image** **compression** at the region of interest and lossy **compression** means quality of **image** degrades in the rest part of the **image**. It is simple and faster method which gives good bpp (bits per pixel) value, high PSNR and lower computational complexity. Arithmetic coding is better than the Huffman coding because it gives fewer bits as level of folding increases. It works comparatively better for smooth images. The proposed work is mainly suitable for medical images. The proposed data folding **algorithm** can be implemented using transformation technique such as DCT, DWT etc where the computation complexity is affordable. This results high **compression** ratio and achieves **lossless** **compression** for complete **image**.

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Abstract: Multispectral **image** acquisition devices produce multi-layer images in which each layer contains the pixel values which are non-negative in nature. **Compression** of these multi spectral **image** aims to transform the **image** into more compact form that is convenient for storage, transmission, processing and retrieval. In this paper a band decomposition and discarding approach is proposed with wavelet and correlation coefficients. The resultant spectral **image** is subjected for spatial binary plane technique based **compression** **algorithm**. The approach is operated in **lossless** mode and compared against traditional JPEG-LS with multiple metrics. Experiments were conducted on several standard multispectral images that are available for research and observed that the proposed method provides an average **compression** ratio of 7.34 which is 1.73 times more than earlier method

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Ming-Sheng Wu et.al [17] have planned an genetic **algorithm** (GA) based on discrete wavelet transformation for fractal **image** **compression**. First, for each range block, two wavelet coefficients were used to find the fittest Dihedral block of the domain block. The similar match was done only with the fittest block to save seven eighths redundant MSE computations. Second, embedding the DWT into the PSO, a PSO based on DWT was built to fast evolutionary speed further and maintains good retrieved quality. Experiments showed that, under the same number of MSE computations, the PSNR of the proposed PSO method was reduced 0.29 to 0.47 dB in comparison with the SGA method. Moreover, at the encoding time, the proposed PSO method was 100 times faster than the full search method, while the penalty of retrieved **image** quality was relatively acceptable.

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Many experiments on the psycho physical aspects of human vision have proven that the human eye does not respond with equal sensitivity to all incoming visual information; some pieces of information are more important than others. Most of the **image** coding algorithms in use today exploit this type of redundancy, such as the Discrete Cosine Transform (DCT) based **algorithm** at the heart of the JPEG encoding standard [3]. 2.2 Types of **Compression**:

233 The above figure shows the **algorithm** used for implementing the proposed technique. „Purelin‟ and „tansig‟ transfer functions are used at the input layer and the hidden layers respectively to train the neurons. The neurons are trained used different learning algorithms viz. Lavenberg Marquardt **algorithm**, quasi-newton method and gradient descent methods for an error limit of 10 -5 . Once the neurons are trained the **image** to be compressed is given to the trained network and the resultant output is found to be compressed in disk size.

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This paper deals with Optimized Binary Merge Coding for data **compression**, which is a modification to the Binary merge coding. Like in BMC the Optimized Binary Merge Coding uses Huffman coding after the modified Binary Merge Coding. The results of the Optimized Binary Merge Coding are compared with Binary Merge Coding and JPEG. An experimental result shows that Optimized Binary Merge Coding improves **compression** rate compared to Binary Merge coding. The same **algorithm** can be extended to color images.

Hence the **compression** is a topic which increases much essentialness and it can be utilized as a part of numerous applications. This theory displays the lossy and **lossless** **image** **compression** on various document organizations of images. Various sorts of techniques have been surveyed in record of amount of **compression** that they offer viability of the strategy utilized and the affectability of error. The viability of the strategy utilized and the affectability to error are sovereign of the element of the gathering of source. The level of the **compression** accomplished significantly relies upon the source record. It is ended that the higher information excess favors to achieve more compacted **image**. The proposed strategy has the benefit of LZW **algorithm** which is joined with the fractal deterioration technique is known for the clearness and quickness. The real objective is to diminish the computational time and limit the space inhabitance.

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In this paper, we introduced a technique for **image** **compression** using shift coding we provided an overview of various existing coding standards **lossless** **image** **compression** techniques. We have proposed a high efficient **algorithm** which is implemented using the shift coding approach. The proposed method takes the advantages of the zigzag with the advantages of the shift coding which is known for its simplicity and speed. The ultimate goal is to give a keep the time and space complexity minimum. The way of **image** **compression** is **lossless** so that’s means in **compression** and decompression will save the quality without any lost. The evaluation of the proposed method shows good performance **image** before **compression** cannot be distinguished from the **image** after **compression**. Also the proposed system operates efficiently and quickly in terms of memory and CPU.

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Hybrid **image** **compression** includes a combination of Lossy and **Lossless** methods [1]. The lossy **compression** method includes the Discrete Cosine Transform (DCT) **algorithm** whereas the **lossless** **compression** method will include Huffman[1], LZW and RLE[2] algorithms and the output will be the compressed **image**. This hybrid **compression** is applied on grayscale medical images. The algorithms will be differentiated based on statistical parameters (CR, Space Saving and Time).

Abstract—This work represents hardware implementation of Lempel Ziv **algorithm** for **lossless** **image** **compression**. In this paper, hardware-based encoder and decoder have been used. In the proposed system Altera DE-I Board have been used for implementation of an **image** **compression** **algorithm**. The architecture of **compression** and decompression **algorithm** design has been created using the hardware description language (VHDL) in Quartus II 9.0. For the processor the supporting software has been written in C is developed. Altera NIOS II 9.0 embedded processor system has been used to perform all hardware interaction tasks necessary on the DE-I board. The custom hardware have been constructed as elements inside the NIOS II system. The experimental results are checked with medical images, stock exchange images, geostationary images and standard images. For the complete analysis, qualitative measures viz. PSNR (peak signal to noise ratio), MSE (Mean square error) and CR (**Compression** ratio) are calculated. The proposed LZW **algorithm** on hardware keeps very significant PSNR, lowest MSE.

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Abstract: In this modern world with developing technology there exist demand for data and information transmission in a safe and rapid manner. There exists a need for compressing these images for storage and communication purposes. **Image** **compression** is to compress the **image** to produce good quality **image** and to reduce the storage space. This **algorithm** will provide efficient **image** **compression** without reducing the quality of **image**. To retain the secrecy of the **image** RSA encryption **algorithm** is used for encrypting and decrypting the **image** in a secured manner followed by SPIHT **algorithm** for decomposition and encoding. Here in this paper ETC and CTE methods are adopted and compared. The **Lossless** **image** Encryption then **Compression** (ETC) system yields high **compression** ratio, high PSNR when compared with CTE system. The performance parameters like **Compression** Ratio, PSNR (in dB), MSSIM are tabulated.

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In recent years, a dramatic increase in the amount of information available in the form of digital **image** data, it become necessary to solve the problems of storage and time issues by utilizing **image** **compression** of redundancy removal based. In general, **Image** **compression** techniques generally fall into two categories: **lossless** and lossy depending on the redundancy type exploited, where **lossless** also called information preserving or error free techniques, in which the **image** compressed without losing information that rearrange or reorder the **image** content, and are based on the utilization of statistical redundancy alone such as Huffman coding, Arithmetic coding and Lempel-Ziv **algorithm**, while lossy which remove content from the **image**, which degrades the compressed **image** quality, and are based on the utilization of psycho-visual redundancy, either solely or combined with statistical redundancy such as vector quantization, fractal, block truncation coding and JPEG [1], reviews of **lossless** and lossy techniques can be found in [2],[3],[4]-[7]. Modelling or a Mathematical Model is a simple description formula utilized efficiently in **image** **compression** problem to remove the correlation embedded between **image** pixel neighbours (spatial/interpixel redundancy). A **compression** system of modelled based, is generally composed of two parts; one corresponds to mathematical function (deterministic part) exploited to create an approximation modelled **image** that resemble the original **image**, and the second part corresponds to the error or residual (probabilistic part) as a difference between original and the approximated. For more details see [8], [9], [10]. Polynomial coding is modelling based technique exploited by a number of researchers as a tool to compress images [11], [12], [13]-[16]. The techniques characterized by simplicity of implementation, efficiency in reducing **image** information into small effective coefficients.

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In this paper, various **compression** techniques are discussed considering both **lossless** and lossy approaches and also the comparison of existing algorithms and their performance for **image** representation. In Lossy approaches using wavelet transforms, lifting scheme take advantage of a spatial domain, prediction error analysis of the wavelet transform & presents an effective structure for designing transform for the purposes of the signal de-noising. Benefits of Wavelet filter banks which map integer to integer are applied for **lossless** **image** **compression**. **Compression** of natural and smooth images are better done with the filter banks having high analyzing vanishing moments rather than images with edge and high frequency components. Performance of the filter banks which adjust to the signal variations by changing the number of zero moments are better than fixed filter banks and is very useful in compact representation of an **image**. The Precise adaptation can be smeared depending on the Least Absolute Deviation (LAD) principle than Least Mean Square (LMS) adaptation. The Set Partitioning in Hierarchical Trees (SPIHT) **algorithm** is used by using considering the lifting scheme for **compression** of the images. An Improvement in PSNR for low bit rates can be accomplished by using Adaptive synthesis filters banks.

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Based Compression Algorithm', The International Arab Journal of Information Technology, vol. R, 'Simulation Based VLSI Implementation of Fast Efficient Lossless Image[r]