Executive summary This article refers to the address: http:// Video cameras have recently appeared in every corner because they allow us to monitor things, such as somewhere, someone, and something, without knowing it. Camera technology is like a human eye. You can only see objects and scenes by feeling and dealing with the light that exists. Just like turning off the lights in the house, we need to work hard to see that many video cameras have to deal with low-light environments. If the camera cannot overcome the low-light conditions and is clearly monitored, arranging the camera in the scene may not provide the visibility we need. Recent developments in video camera technology, especially the development of low-light noise filtering techniques, not only enhance the quality of a single still image or frame that makes up a video stream, but also enhance the fidelity of the individual pixels that make up each frame. The improved filtering technology supports 3D work, eliminating visual anomalies in low-light video streams and making images sharp and sharp. Cameras with powerful real-time processors and advanced compression algorithms are usually sharper than human eyes in low-light conditions. According to the 2010 FBI Unified Crime Report, 52.8% of violent crimes and 81.7% of property crimes have not been uncovered. A camera with good low-light technology will help to reduce this number. Part of the cause of the problem Video cameras (IP cameras) connected to the Internet are being deployed in a variety of applications. In addition to the common security surveillance cameras implemented in security and law enforcement facilities, cameras are now also widely used in computers, tablets and smartphones. In addition, more and more vehicles have begun to be equipped with video surveillance systems or car black boxes to assist in complex operations in narrow corners and enhance vehicle safety. As a basis for these and other applications, IP cameras must provide video streams frequently in low light, even with auxiliary lighting. This issue is especially important in security applications. IP surveillance cameras used to monitor dark alleys or dimly lit corridors are often blurred, pixelated, unclear, and have poor contrast. Low-quality video streams often cause the face to be unrecognizable or completely miss the events that occur, affecting the usefulness of the video security monitoring system. Providing better lighting conditions is generally not feasible because the lighting itself requires cost and maintenance and makes the safety monitoring system easy to notice. Finally, the ideal solution is to enhance the low-light performance of IP cameras, enabling them to achieve higher productivity in lower light and provide crisp and sharp images. Of the images below, one uses low-light performance enhancement technology and the other does not. They clearly illustrate how these technologies enhance the effectiveness of applications such as video surveillance systems. Figure 1: Video images not taken with low-light performance enhancement technology Figure 2: Video images taken with low-light performance enhancement technology "noise" in low-light video signals The culprit causing the degradation of image quality in IP cameras under low-light conditions is “noiseâ€. Or it is the irrelevant signal data of the camera that is not included in the image due to low-light conditions. Artifacts or anomalous data in the signal processing of these cameras can result in reduced video stream quality, blurred images, and reduced sharpness. To improve the image quality of video IP cameras operating in low-light environments, it is necessary to eliminate this noise as much as possible. By increasing the signal-to-noise ratio (SNR) of the video stream, the camera provides sharper images. When the noise is reduced, the signal from the actual object in the scene will be enhanced and the video image will be sharper. There are several clear sources of noise, each of which must be treated to some degree. · Particle noise Particle noise is of course due to fluctuations in the hit rate of the visible light sub-video camera sensor. The number of photons hitting the sensor pixel varies randomly around an average rate that is proportional to the illuminance of the pixel. · Fixed graphics noise The fixed pattern noise is caused by small variations in the sensor pixels that make up the IP camera. Each pixel reacts differently when hit by a visible light. These differences may come from differences in pixels and color filters, or from differences in circuits that connect pixels. · Read out noise The analog information collected by the video camera light sensor must be converted to digital data for camera processing. This work is done by an analog to digital converter. Read out the noise caused by the incompleteness of the conversion process. Eliminate noise There are two basic ways to eliminate or filter the noise of a low-light video stream. The first method is spatial filtering. This method is performed along the two-dimensional, high, and wide constituting the image of the display screen. The second method is temporal filtering, which adds a third dimension to the time that still exists in the video stream. · Spatial filtering A video stream consists of separate still images or frames that are played in chronological order. Real-time video streams typically display 30 frames per second. However, other display rates can be implemented if the actual action is not required. Spatial filtering is an algorithmic analysis that checks each frame independently, compares each pixel along the X and Y axes of the image, and finds noise and removes it. · Time filtering The temporal filtering algorithm adds the time dimension to the analysis. This method does not examine the entire frame like spatial filtering, but analyzes each pixel in time dimension. That is, each pixel is compared with the next frame to determine whether noise exists. If a pixel moves much like noise, it is cleared. Time filtering is more complicated than spatial filtering because video streams often contain motion, such as moving objects or someone walking through the camera's field of view. Time filtering must not only be able to distinguish the real motion that the camera needs to observe, but also keep it in the video data stream. At the same time, it should also find any type of abnormal motion in the pixel that may be noise, and clear it. To achieve this, two methods have been developed, namely motion adaptive method and motion compensation method. The motion adaptive method can attempt to determine the region of the video stream in which the motion of the object occurs. Signal transmissions in these areas are preserved, including any noise that may occur in these areas. Analyze the moving pixels in other parts of the image to determine if it is noise, and if so, clear it. The motion compensation analysis method is much more complicated than the motion adaptive method. The motion complement analysis method will first establish a reference frame to temporally filter the motion pixels formed by the movement of the object. If the motion analysis is correct, the motion compensation method can remove more noise on the moving object than the motion adaptive method. However, motion analysis is prone to errors at high noise levels formed under low illumination conditions, so noise may be removed and annoying artifacts may be generated. Technology providers such as Texas Instruments, which offer a wide range of motion-adaptive and motion-compensated methods, can meet the needs of IP camera manufacturers and security system integrators. Low light performance advantage IP cameras capable of capturing high-quality video in low-light environments are extremely efficient in a variety of applications, especially in security surveillance systems, which typically operate in low-light environments and may further reduce the video's inclusion. Information level compression. All IP cameras use some form of video compression, often using the H.264 high-end class (HP) codec for best-in-class compression efficiency. Video compression can result in loss of information. To keep the compressed video at an acceptable quality, such as a car license plate that passes through dimly lit streets or its driver's face, keep the compressed video bit rate as high as possible. However, the amount of storage and the required backup time constrain the bit rate of low-cost video compression. To achieve the required video quality, high-noise video requires more data to be compressed, which requires more storage, which in turn increases equipment costs. If you can't increase storage, you should reduce backup time and keep compressed video at an acceptable quality. Similarly, if video must be transmitted across the network with limited bandwidth, less high-noise video can be transmitted at higher quality within the limits of available bandwidth. This is especially important for use in residential homes because there are multiple Wi-Fi hotspots competing for a small amount of available spectrum. Compression techniques such as H.264 rely on consistency between multiple video frames to compress the information contained. Noise can cause a high degree of inconsistency in multiple frames, requiring more data to compress compressed video information. Good low-light technology eliminates inconsistent noise and achieves acceptable video quality at lower bit rates. IP camera security system operators can record higher quality video, extend backup time, reduce storage costs or use constant storage capacity. In addition, operators of IP camera security systems that monitor low-light outdoor doors may also consider installing more external lighting to improve lighting in the area, thereby improving video stream quality. Since cameras with low illumination can compensate for lighting conditions, no additional lighting is required. This eliminates the need to install new wire lights and replaces the original security camera with an IP camera with excellent low-light performance. In addition, incremental power costs due to the installation of more external lighting can be completely avoided. Many advanced monitoring systems also incorporate an automated security analysis process that further processes the video stream and provides accessibility to system operators responsible for monitoring. These analysis features are more efficient at processing clearer, higher definition video streams. Better security analysis reduces the human resources of operating a safety monitoring system. Figure 3: High-noise video image without low-light performance enhancement technology Figure 4: Video image with TI low-light noise filter TI Low Light Performance Solution TI has proven proven performance in providing industry-leading technology for video security processing applications, such as IP-based video security surveillance cameras. The DaVinciTM video processor platform is the industry's highest performance video engine, enabling video and graphics accelerators to simultaneously process three 1080p 60fps video streams for new applications and an intuitive user interface. DaVinci users get a scalable line of products on a single platform with the same core IP, offering a unique combination of high performance, low power and multiple derivatives for specific applications. TI's reference design enables fast product development in just six months. In addition to a large number of supporting devices, TI's best-in-class video processors have been at the heart of many IP cameras. The recently introduced DaVinci DM385 video processor goes one step further and integrates more powerful low-light performance, including 3D noise filtering, wide dynamic processing and SVC-T H.264 high-end compression technology with the best compression efficiency in the industry. . The DM385 supports H.264 or SVC-T high-end codecs that compress real-time video streams up to 4 megapixels and secondary D1 live video streams. 4Kx2K or higher resolution video, simultaneous multi-level (basic/mainstream/advanced) compression, face detection, video stabilization, multiple exposure and dual channel sensor support help camera manufacturers make their products easy on the market Differentiate the ground. In addition, the DM385 IP Camera Reference Design helps manufacturers quickly bring to market the latest low-light cameras while taking advantage of any of the most common sensors. (DaVinci DM385 video processor block diagram) Wind Turbine system, wind turbine generator system, wind turbine blade, maglev wind turbine, vertical axis wind generator, windmill generator, wind turbine generator types, vertical axis wind turbine, Vertical Generator Wind Turbine, vertical wind generator, Vertical Wind Turbine, Wind Power Generator for home, wind turbine generator kit VAWT Vertical Axis Wind Turbine VAWT Vertical Axis Wind Turbine, VAWT Vertical Windmill Generator,VAWT Vertical Wind Turbine Generator Types, VAWT Vertical Axis Wind Turbine,Vertical Generator Wind Turbine Delight Eco Energy Supplies Co., Ltd. , https://www.cndelight.com
