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  • 1. P.Naga`Vardini, T.Giri Prasad / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.1019-1024 Research of Image Acquisition and Decompressing Based on ARM9 System P.Naga`Vardini*, T.Giri Prasad** *M.Tech student, ECE.Dept, PKSK College **ASSOC. prof, ECE.Dept PKSK collegeAbstract:- Currently on the market most of the Looking forward to new era of imageimage acquisition system are based on DSP. These processing, modernized and fastest ways toimage acquisition systems are high cost, great compress the images by using JPEG , in the worldpower consumption, and volume restriction, due to of photography and its best outputs in terms ofthese limitations which dsp processors are not image compression ,world is definitely in need ofsuitable for some simple applications. With the new approaches. To achieve this we need todevelopment of image processing technology, evaluate the performance of ARM PROCESSORimage acquisition system which based on ARM is by using JPEG as one of the application. This is anmore and more popular. Every where commonly embedded approach for the compression which canused image compression technique is jpeg. So be useful and suitable for the application likeimplementation of such jpeg decoder is more digital camera. Now a days Digital camera hasuseful. Using these jpeg decoder one can decode become fastest and best means in the world ofjpeg images of any compression ratio can decode photography, and so as the images created by it.the jpeg images and displays output on TFT LCD Figure 1 shows generalized block diagram of ARMdisplay. This paper developed such jpeg decoding based JPEG encoder. As any Images file created byalgorithm on ARM processor. Which is portable digital is to be compressed by using JPEG standardand efficient as well. and then further this file will be processed through serial communication by using ARM processor.I. INTRODUCTION JPEG acts as a JPEG encoder for the ARM Real-time embedded systems are a challenge processor. In this paper, evaluating the performanceto implement. An embedded environment of ARM processor family with JPEG. Here, imageconstrains the energy consumption, the memory file is taken from windows and then converted intospace, and the execution time of an application. On grayscale image by using MATLAB then is processthe other hand, the complexity of theapplications through ARM.running on an embedded platform keeps increasing.Examples include next-generation cell phones, II. ARM PROCESSORportable gameboys and digital assistants, An Embedded Processors is simply asmartcards, and medical monitoring devices. micro Processors that has been “Embedded” into a device. It is software programmable but interacts with different pieces of hardware. The three most important design criteria for embedded processors are performance, power, and cost. Performance is a function of the parallelism, instruction encoding efficiency, and cycle time. Power is approximately a function of the voltage, area, and switching frequency also a function of execution time. cost is a function of both area (how many fit on a die) and the complexity of use (in terms of engineering cost).ARM seems to be leading the way in this field of processing. The processor has found this as one of its greatest markets, mainly because of the steps the company has taken to fit into the embedded market and the architecture it has adopted. Other embedded applications that take advantage of such processors are: disc drive controllers, automotive engine control and management systems, digital auto surround sound, TV-top boxes and internet appliances. Other products are still being modified to take advantage of it: toys, watches, etc. The possible applications 1019 | P a g e
  • 2. P.Naga`Vardini, T.Giri Prasad / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.1019-1024are almost endless. ARM can offer low cost, high method to date, and is sufficient in its own right forperformance and low power consumption, each of a large number of applications. JPEG has followingwhich is required to make a portable embedded modes of operation:item marketable in todays world. Not to mentionthe fact that a whole subgroup of ARM architecture a) Sequential encoding: each image component ishas been dedicated to function strictly to signal encoded in a single left to right top to bottomprocessors. scan. b) Progressive encoding: the image is encoded in2.1 FEATURES OF ARM PROCESSOR multiple scans for applications in whichFOLLOWING ARE THE FEATURES OF THE transmission time is long, and the viewerARM PROCESSOR. prefers to watch the image build up in multiplea) On-chip integrated oscillator operates with coarse to clear passes. external crystal in range of 1 MHz to30 MHz c) Lossless encoding: the image is encoded to or with external oscillator from 1 MHz to 50 guarantee exact recovery of every source MHz. image sample value (even though the result isb) Power saving modes include idle and low compression compared to the lossy Powerdown. modes).c) Individual enable/disable of peripheral d) Hierarchical encoding: the image is encoded at functions as well as peripheral clock scaling multiple resolutions so that lower resolution down for additional power optimization. versions may be accessed without first havingd) Two 32-bit timers/external event counters to decompress the image at its full resolution. (with four captures and four compare channels each), PWM unit (six outputs) and watchdog. 3.1 BLOCK DIAGRAM DESCRIPTION OFe) Low power Real-time clock with independent JPEG power and dedicated 32 kHz clock input. Figure2 shows the key processing steps which aref) Multiple serial interfaces including two the heart of the DCT Based modes of operation. UARTs (16C550), two Fast I2C (400 kbit/s), These figures illustrate the special case of Single SPI™ and SSP with buffering and variable component (grayscale) image compression. Color data length capabilities image compression can then be approximatelyg) Vectored interrupt controller with configurable regarded as compression of multiple grayscale priorities and vector addresses. images, which are either compressed entirely one ath) Up to 47 of 5 V tolerant general purpose I/O a time, or are compressed by alternately pins in tiny LQFP64 package. interleaving 8x8 sample blocks from each in turn.i) Processor wake-up from Power-down mode For DCT sequential mode codecs, which include via external interrupt or Real-time Clock. the Baseline sequential codec, the simplifiedj) Single power supply chip with Power-On diagrams indicate how, single component Reset (POR) and Brown-Out Detection (BOD) compression works in a fairly complete way. Each circuits:– CPU operating voltage range of 3.0 8x8 block is input, makes its way through each V to 3.6 V (3.3 V ± 10 %) with 5 V tolerant processing step, and yields output in compressed I/O pads. form into the data stream.III. JOINT PHOTOGRAPHY EXPERT GROUPS For the past few years, a joint ISO/CCITTcommittee known as JPEG (Joint- PhotographicExperts Group) has been working to establish thefirst international compression standard forcontinuous tone still images, both grayscale andcolor. JPEG‟s proposed standard aims to begeneric, to support a wide variety of applicationsfor continuous tone images. To meet the differingneeds of many applications, the JPEG standardincludes two basic compression methods, each withvarious modes of operation. A DCT based methodis specified for “lossy‟‟ compression, and apredictive method for “lossless‟‟ compression. DCT progressive mode codecs, an imageJPEG features asimple lossy technique known as buffer exists prior to the entropy coding step, sothe Baseline method, a subset of the other DCT that an image can be stored and then parceled out inbased modes of operation. The Baseline method multiple scans with successively improving quality.has been by far the most widely implemented JPEG For the hierarchical mode of operation, the steps 1020 | P a g e
  • 3. P.Naga`Vardini, T.Giri Prasad / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.1019-1024shown are used as building blocks within a largerframework. The DCT coefficient values can thus beregarded as the relative amount of the 2D spatialfrequencies contained in the 64point input signal.The coefficient with zero frequency in bothdimensions is called the “DC coefficient” and theremaining 63 coefficients are called the “ACcoefficients.‟‟ . Figure 4 shows that DCT operationand related equation. For ARM Processor,Floatingpoint arithmetic is a more flexible andgeneral Mechanism than fixed-point. With floating-point, system designers have access to widerdynamic range (the ratio between the largest andsmallest numbers that can be represented). As aresult, floating-point are generally easier toprogram than their fixed point but usually are alsomore expensive and have higher power These coefficients are directly used in Keilconsumption. The increased cost and power software for embedded version. Since we can useconsumption result from the more complex JTAG for this but it increases C code, so directlycircuitry required within the floating-point which taking image coefficients to process for DCT.implies a larger silicon die. Table 3 shows DCT values for sample image and its is perfectly matched with ARM. This DCT code is written as per R [1]. In „C‟ code all the last values are get divided by 65535 because when we are converting sample image into gray we are using index 16 and in MATLAB it is index 16 is for 65535. 3.2 Quantization After output from the DCT, each of the 64 DCT coefficients is uniformly quantized in conjunction with a 64element Quantization Table, which must be specified by the application (or user) as an input to the Encoder. Each element can be any integer value from 1 to 255, which specifies the step size of the quantizes for its corresponding DCT coefficient. The purpose of quantization is to 1021 | P a g e
  • 4. P.Naga`Vardini, T.Giri Prasad / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.1019-1024achieve further compression by representing DCTcoefficients with no greater precision than isnecessary to achieve the desired image quality.Stated another way, the goal of this processing stepis to discard information, which is not visuallysignificant. Quantization is a many to one mapping,and therefore is fundamentally lossy. It is theprincipal source of lossiness in DCT basedencoders. Quantization is defined as division ofeach DCT coefficient by its correspondingquantized step size, followed by rounding to thenearest integer: F„ [u, v] = round (F [u, v] / q [u, v]). Why? -- To reduce number of bits per sample Example: 101101 = 45 (6 bits). Q [u, v] = 4 --> Truncate to 4 bits: 1011 = 11. For the ARM Processor, we are quantizingFile i.e. reducing file size length. Memory required, Here, all the results of DCT andexecution time, speed required will be less for this quantization of Keil and MATLAB are perfectlyblock. Here four table specifications are required matched. Here, we have used MATLAB only forfor quantization. Each table has 64 byte, so total verification of keil output. Since, algorithm used256 bytes are required. For quantization Luminous for JPEG for embedded platform should betable for color image is used because it is same for matched with standard tool that‟s why we havegray image. Table 4 shows quantization table for used MATLAB. Up to quantization we can see thegray scale image and Table 5 shows keil output of output of the image after that image coefficients arequantization and it is again is perfectly matched scanned or prepared for entropy encoding. Fig 5with MATLAB. shows quantized output and image size. 3.3 DC Coding and Zig -Zag Sequence After quantization, the DC coefficient is treated separately from the 63 AC coefficients. The DC coefficient is a measure of the average value of the 64 image samples. Because there is usually strong correlation between the DC coefficients of adjacent 8x8 blocks, the quantized DC coefficient is encoded as the difference from the DC term of the previous block in the encoding order. Figure 6 shows zigzag scanning of an image. 1022 | P a g e
  • 5. P.Naga`Vardini, T.Giri Prasad / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.1019-1024 intialize serial port and through hyper terminal output is shown.fig 7 shows that out of 64 coefficients 47 coefficients are zeros,hence image is successfully compressed because we are using JPEG lossy mode. All of the quantized coefficients areordered into the “zigzag” sequence. This orderinghelps to facilitate entropy coding by placing lowfrequency coefficients (which are more likely to benon zero) before high frequencycoefficients.Here,2D to 1D is converted and thenagain it is converted image coefficients are samebut is zig-zag sanned.3.4 Entropy Coding The final DCT based encoder processingstep is entropy coding. This step achievesadditional compression losslessly by encoding thequantized DCT coefficients more compactly basedon their Statistical characteristics. The JPEGproposal specifies two entropy. Coding methods byHuffman coding and arithmetic coding. TheBaseline .Sequential codec Uses Huffman coding,but codecs with both methods are specified for allmodes of operation. It is useful to consider entropycoding as a 2step Process. The first step convertsthe zigzag sequence of quantized coefficients intoan intermediate sequence of symbols. The secondstepconverts the symbols to a data stream in which thesymbols no longer have externally identifiableboundaries. The form and definition of theintermediate symbols is dependent on both theDCT based mode of operation and the entropycoding method. Huffman coding requires that oneor more sets of Huffman code tables be specifiedby the application. The same tables used tocompress an image are needed to decompress it.Huffman tables may be predefined and used withinan application as defaults, or computed specificallyfor a given image in an initial statistics gathering.Pass prior to compression. Such choices are the LPC2148 has 32KB in addition with 8KBbusiness of the applications which use JPEG; the memory and 512KB on chip FLASH memory. SoJPEG proposal specifies no required Huffman selecting LPC2148 for future work related to colortables. Huffman table needs 64 bytes for encoder images.and 64 bytes for decoder. Table specification isdifferent for color and grayscale image. The final Conclusionimage and output after zig-zag is as shown in JPEG is not the application of ARM7 processorfig3.10 and 3.11. Fig3.10 shows the output of zig- since this processor is not in the area of imagezag scanning its values are fixed to near zero by compression. In this work, after successfulsuing MATLAB n same id done with the help of experimentation on JPEG lossy technique, codekeil embedded software. Fig 3.11 shows output word is obtained using Huffman coding. Outcomecodeword of sample image.For keil we have to of this work shows that code word is generated by 1023 | P a g e
  • 6. P.Naga`Vardini, T.Giri Prasad / International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.1019-1024scanning quantization matrix. Using this technique, [17]. JPEG Technical Specification, AdobeJPEG lossy technique is successfully implemented Developer Support year1992.on keil platform to assert the Uart signal of [18]. “INFORMATION TECHNOLOGY JPEGLPC2148 processor, so as to show the output on 2000 IMAGE CODING SYSTEM”, JPEGhyper terminal. Image compression is nearly about 2000 FINAL COMMITTEE DRAFT90% to 95%. VERSION 1.0, 16 MARCH 2000. [19]. Michael D. Adams and Faouzi Kossentini,”References: JasPer: A Software-Based JPEG-2000[1]. www.Xillinx.com Codec Implementation”.[2]. Gunnar Braun , Achim Nohl, Andreas [20]. INFORMATION TECHNOLOGY,” Weiferink ,”Processor/ Memory Co- DIGITAL COMPRESSION AND CODING Exploration on Multiple Abstraction OF CONTINUOUS-TONE STILL Levels”,IEEE 2003. IMAGES”, REQUIREMENTS AND[3]. Gregory K. Wallace,” The JPEG Still GUIDELINES, CCITT Rec T.81. Picture Compression Standard”, IEEE 1991 [21]. Dongdong Fu, Yun Q. Shi, Wei Su,” A[4]. Ieeexplore.ieee.org/ie15/8483/26600/012537 generalized Benford‟s law for JPEG 30.pdf. coefficients and its applications in image[5]. CHANG-AN TASI,LAN –DA VAN ,”ARM forensics”, Newark, NJ 07102, USA BASED SOC PROTOTYPING PLATFORM USING APTIX “,IEEE 5 MARCH 2005[6]. Bart Weuts, “Software for ARM Processors and AMBA Methodology-based Systems”, Volume 3, Number 4, 2004[7]. J,Weinberger and Gadiel Seroussi,” lossless Image compression Algorithm principles and standardization. The loco –I into JPEG- LS “.[8]. For MATLAB investigation http://www.stanford.edu/class/ee 68b / Prrojects / jcsabat / Mcpperf.html[9]. Jike Chong , Abhijit Dawore, Kelvin Lwi,” Concurrent Embedded design for multimedia:JPEG encoding on xillinx FPGA”,EECS, April 16,2006[10]. www.cs.vcsd.edu/classes/spo2/cse29/- e/slides/armlect.pdf.[11]. Krishna swami ,Gupta,” Profile guided selection of ARM and THUMB instruction”2002.[12]. Rishi Bhardwaj, Phillip Reames, Russell Greenspan Vijay Srinivas Nori, Ercan Ucan” A Choices Hypervisor on the ARM architecture”,2003[13]. Diego Santa-Cruz and Touradj Ebrahimi,” AN ANALYTICAL STUDY OF JPEG 2000 FUNCTIONALITIES”, IEEE. 2000[14]. Kai-Yuan Jan*, Chih-Bin Fan, An-Chao Kuo, Wen-Chi Yen and Youn-Long Lin,” A platform based SOC design methodology and its application in image compression”, Int. J. Embedded Systems, Vol. 1, Nos. 1/2, 2005 23[15]. Yu-Lun Huang and Jwu-Sheng Hu,” A Teaching Laboratory and Course Programs for Embedded Software Design”, iCEER- 2005.[16]. Hany Farid,” Digital Image Ballistics from JPEG Quantization”, TR2006-583. 1024 | P a g e