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VLSI IEEE 2018 Projects at Chennai

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IMAGE scaling is widely used in many fields, ranging from consumer electronics to medical imaging. It is indispensable when the resolution of an image generated by a source device is different from the screen resolution of a target display. For example, we have to enlarge images to fit HDTV or to scale them down to fit the mini-size portable LCD panel. The most simple and widely used scaling methods are the nearest neighbor and bilinear techniques. In recent years, many efficient scaling methods have been proposed in the literature.

According to the required computations and memory space, we can divide the existing scaling methods into two classes: lower complexity and higher complexity scaling techniques. The complexity of the former is very low and comparable to conventional bilinear method. The latter yields visually pleasing images by utilizing more advanced scaling methods. In many practical real-time applications, the scaling process is included in end-user equipment, so a good lower complexity scaling technique, which is simple and suitable for low-cost VLSI implementation, is needed. In this project, we consider the lower complexity scaling techniques only. Kim presented a simple area-pixel scaling method. It uses an area-pixel model instead of the common point-pixel model and takes a maximum of four pixels of the original image to calculate one pixel of a scaled image. By using the area coverage of the source pixels from the applied mask in combination with the difference of luminosity among the source pixels, Andreadis proposed a modified area-pixel scaling algorithm and its circuit to obtain better edge preservation. Both obtain better edge-preservation but require about two times more of computations than the bilinear method. To achieve the goal of lower cost, we present an edge-oriented area-pixel scaling processor in this paper. The area-pixel scaling technique is approximated and implemented with the proper and low-cost VLSI circuit in our design. The proposed scaling processor can support floating-point magnification factor and preserve the edge features efficiently by taking into account the local characteristic existed in those available source pixels around the target pixel. Furthermore, it handles streaming data directly and requires only small amount of memory: one line buffer rather than a full frame buffer. The experimental results demonstrate that the proposed design performs better than other lower complexity image scaling methods in terms of both quantitative evaluation and visual quality.

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