International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Data Acquisition Using Camera Serial Interface Diksha Sagar1, Dr. Jeeru Dinesh Reddy2 1PG Student, Dept. of Electronics and Communication Engineering, BMS College of Engineering, Bengaluru, India 2Professor, Dept. of Electronics and Communication Engineering, BMS College of Engineering, Bengaluru, India
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Abstract - Reaching high integration, high speed, high
applications support JPEG and sRGB picture formats. These days, the majority of cameras enable the saving of photos in RAW format, which is an unprocessed, minimally compressed picture format that captures the reaction from the camera sensor. More benefits of RAW over sRGB include a broader color gamut, a higher dynamic range (usually 12– 14 bits), and a linear response to scene radiance. For several computer vision applications, including white balance, photometric stereo, picture restoration, and more, RAW is preferred. Photographers also prefer RAW because it gives them more versatility when manipulating images in postprocessing. The serial process of compression of images starts with the conversion of an RGB image into YIQ if required. The resulting image is then transformed by DCT. In the quantization, unnecessary data about the image is eliminated from size and quality. Encoding of the image is done for protection by changing the names of the values of the quantized image by passing the image into the channel encoder. The image is involved in inverse quantization, which retrieves the lost data from the image. Passing through the inverse transformation phase forms the original image. Image compression is a technique that lowers the amount of data needed to communicate with an advanced image. And eliminating the redundant workers will provide this.
resolution, and high reliability is the aim of image preprocessing systems. Image processing systems are widely employed in both the military and commercial industries. Image processing technology-based object detection has drawn a lot of interest in the military because of its noncontact capabilities, capacity to hide, and ability to avoid interference. In the business sector, it is widely used in industrial detection systems and machine vision. There are three main kinds of image processing systems that are used to implement digital image processing techniques. The three main chips that comprise each system are the FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor chip), and ASIC (Application Specific Integrated Circuit). In this work, we created an image processing system based on FPGA. The system can take samples from the data stream.
Key Words:
FPGA, Image acquisition system, Image processing, Xilinx Vivado HLS, MATLAB, Verilog.
1. INTRODUCTION Artificial intelligence, pattern recognition, and signal processing are all engaged in the study of picture collection and processing, which has been a popular area of research. This technology is mainly used in automotive electronics, consumer electronics, security monitoring, national defence, and other fields of 3D projection. The increasing popularity of digital image processing technology is inseparable from the perfecting of processing systems. In the image processing system, the key technology is real-time image acquisition and processing. Meanwhile, the speed and quality of image acquisition directly affect the system [10]. The advancement of large-scale integrated circuit fabrication technologies, particularly FPGA, and microelectronics has produced innovative concepts and techniques for enhancing the functionality of image processing systems in recent years. The image processing system based on FPGA is widely utilized in the image preprocessing area because of the vast amount of data and rapid processing speed required for lowlevel picture preprocessing. The need for video information has increased as a result of the advancements made in multimedia technologies in recent years. In any case, the significance of picture processing and capture is growing. 8bit standard RGB (sRGB) pictures, which are commonly compressed using the JPEG standard, make up the great majority of images used in computer vision and image processing applications. The processes of almost all imaging
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Operation of the JPEG Encoder core: 1.1 Color Space Transformation The first operation of the JPEG Encoder core is converting the red, green, and blue pixel values to their corresponding Luminance and Chrominance (Y, Cb, and Cr) values. The RGB2YCBCR module is where this procedure is carried out. The operation is based on the following formulas:
Y = .299 * Red + .587 * Green + .114 * Blue Cb = -.1687 * Red + -.3313 * Green + .5 * Blue + 128 Cr = .5 * Red + -.4187 * Green + -.0813 * Blue + 128 Fixed point multiplications are used to carry out these tasks. All of the constant values in the above 3x3 matrix are multiplied by 2^14 (16384). One clock cycle is used for the multiplications, and the next clock cycle is used to add the sum of the products. In order to obtain a quick
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