Using multi-phase interpolation algorithm to achieve video image scaling and its verification on FPGA hardware platform

**Abstract**: Traditional interpolation algorithms often struggle with preserving details when scaling video images, especially at high resolutions. To address this issue, a multi-phase interpolation algorithm is employed for video image scaling. This paper explains the working principle of the algorithm and its corresponding hardware architecture. The system is implemented using Xilinx Spartan6 series FPGA chips to control the hardware circuit. It supports scaling video signals from four cameras, regardless of the input channel, to a resolution of 1920x1080 @ 60 Hz. The experimental results demonstrate that the output video is real-time and maintains excellent visual quality with clear details.  **0. Preface** Video image scaling, also known as resolution conversion, resampling, or scale conversion, is a crucial technology in digital video processing. It directly impacts the quality and visual experience of the output video. This technique is widely applied in fields such as medical imaging, engineering, multimedia, and video conferencing [1]. Traditional video scaling methods include nearest neighbor, bilinear, and bicubic interpolation. other approaches like edge-based interpolation, B-spline interpolation, and adaptive interpolation are also used. However, these methods can introduce artifacts, especially when dealing with high-resolution outputs, leading to loss of detail and poor visual quality. Multi-phase interpolation, on the other hand, offers better preservation of image details and is widely adopted in industrial applications due to its superior performance. **1. Algorithm Principle** The basic idea behind video image scaling is to convert an original image with resolution (M, N) into a target image with resolution (X, Y). Mathematically, this can be expressed as: Pixelout(x, y) = f(i, j, Pixelin(i, j)), where i and j are coordinates of the original image, and x and y are coordinates of the target image. The essence of multi-phase interpolation lies in calculating pixel values based on the mapping function between the input and output images. Since image content has local correlation, the output pixel value depends not only on nearby pixels but also on those further away. The general process involves a two-dimensional filtering operation, which can be represented by the following formula: Pixelout(x, y) = ∑_{i=0}^{HTaps-1} ∑_{j=0}^{VTaps-1} Pixelin(x - HTaps/2 + i, y - VTaps/2 + j) × Coef(i, j) In this equation, HTaps and VTaps represent the number of taps in horizontal and vertical directions, respectively, while Coef(i, j) is the filter coefficient that determines how much each input pixel contributes to the output. These coefficients are crucial in determining the final scaling result and are determined based on low-pass filtering and anti-aliasing requirements. To simplify the computation, the two-dimensional filter is often split into two one-dimensional filters, cascading horizontally and vertically. This approach reduces the number of multipliers required and significantly lowers computational complexity, making it ideal for real-time systems. This design is commonly used in various hardware implementations of video scaling algorithms.  **2. System Hardware Structure** The hardware architecture of the system is illustrated in Figure 1. A Xilinx Spartan6 series FPGA is used to implement the multi-phase interpolation algorithm. The system uses four analog cameras as the input source, and the TW2867 multi-channel video decoder converts the analog signals into digital format before sending them to the FPGA for processing. Before scaling, the FPGA performs preprocessing on the incoming data. Since the input frame rate may not match the output display rate, the image data is first stored in DDR3 memory and then read for scaling and output. Finally, the SIL9134 video encoder transmits the processed signal to the display screen, allowing users to view the output effect. **2.1 Introduction to Related Chips** **2.1.1 Video Decoding Chip** The TW2867 chip plays a key role in converting analog video signals from the cameras into digital format. It supports multiple video standards and ensures accurate synchronization between the input and output signals. Its high-speed interface allows efficient data transfer to the FPGA, making it suitable for real-time processing tasks. **2.1.2 FPGA Chip** The Xilinx Spartan6 series FPGA is chosen for its balance between performance and cost. It provides sufficient logic resources and I/O ports to handle the complex operations involved in multi-phase interpolation. Additionally, its low power consumption makes it ideal for embedded video processing systems. **2.1.3 Video Encoding Chip** The SIL9134 chip is responsible for encoding the scaled video data into a standard video stream. It supports various output formats and ensures compatibility with different display devices. Its high bandwidth and low latency make it well-suited for real-time video transmission.

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The sound file can be from 1second to 480seconds

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Recording time:6seconds,10seconds,20seconds,30seconds,60seconds,90seconds,120seconds,180seconds etc

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F. Waterproof Music Module --which usually are used by Children Cloths,Kids'Bib ,attract the children's attention or Sing the children's songs.

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6) shadow sensor,if somebody walks front it,it will play the message.

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