In this paper, the AMBTC image compression method is proposed which employs three alternative weights to predict the initial center. AMBTC is a simple but efficient compression algorithm, whereas in terms of the image with salt and pepper noise, the visual quality is not satisfying provided that the mean value is used as the threshold in each 4×4 block. The possible reason accounting for this drawback is that image pixels are modified while the mean value can only best represent the original image data rather than noise data. On the contrary, the median value is more proper to represent noise data alone, which indicates that the mean value alone is unable to properly represent the whole image added with noise. That is to say, when the weighted sum of the mean value and the median value is taken as the initial center, the deviation of image added with salt and pepper noise will be reduced. What’s more, noises with various intensities are added to each image in the standard database, which intends to search for the optimal center in each block, and make a mapping between intensities and weights. It is feasible for us to directly use three weights and analyze the noise intensity to predict the initial center. PSNR of an AMBTC image is improved with 1 dB on average. In order to further improve the visual quality, we search for the optimal center in the confidence interval around the initial center so that PSNR can be improved by 3 dB. The experimental results reveal that the proposed fixed-point prediction compression algorithm enhances the image quality and reduces the computational cost to a large extent.
Laser triangulation is often the best solution for the 3D (three-dimensional) image reconstruction of an object. And its features, such as simple structure, fast reconstruction speed and flexible use make it widely used in 3D reconstruction. However, due to its high image frame rate, huge data, and requiring a large amount of computation for image processing algorithm, it is hard to be applied in embedded system. This paper presents a hardware solution for high-speed image acquisition system which is implemented on Xilinx FPGA. The experimental results indicate that this acquisition system works normally and its performance is steady and reliable, under the extreme conditions with an image resolution of 1280*384 and a frame rate of 9,000. It is still a challenging problem to extract the position information of the light bar in real time. In order to solve this problem, a parallel gray center gravity method based on FPGA is proposed to detect the position of light bar in this paper. Test results show that the proposed method can correctly extract the position information of the light bar in real time when the image frame rate reaches 9,000 frames. The power consumption is only about 4 watts.
Because of the advantages of high sampling rate, high-speed image acquisition system is widely used in military, sports, biological and other fields. Unfortunately, just due to its high frame rate, the design risk and design cycle are greatly increased, and the back end image processing is still a challenging problem. In order to solve this problem, an image acquisition system architecture and protocol is proposed in this paper, which divides the image acquisition system into control plane and processing plane. The processing plane is divided into sensor independent layer, cache layer, processing layer and application layer. These layers are physically connected by AXI bus, and the logical relationship between them is as small as possible. Our proposed architecture and protocol effectively reduces the design complexity and design risk, also the design efficiency is greatly improved.
Proc. SPIE. 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015)
KEYWORDS: Signal to noise ratio, Data compression, Image compression, Detection and tracking algorithms, Data modeling, Aerospace engineering, Imaging systems, Data storage, Synthetic aperture radar, Quantization
Because of simple and good performance, the block adaptive quantization (BAQ) algorithm becomes a popular method for spaceborne synthetic aperture radar (SAR) raw data compression. As the distribution of SAR data can be accurately modeled as Gaussian, the algorithm adaptively quantizes the SAR data using Llyod-Max quantizer, which is optimal for standard Gaussian signal. However, due to the complexity of the imaging target features, the probability distribution function of some SAR data deviates from the Gaussian distribution, so the BAQ compression performance declined. In view of this situation, this paper proposes a method to judge whether the data satisfies Gaussian distribution by using the geometrical relationship between standard Gaussian curve and a triangle whose area is equal to that of the Gaussian curve, then getting the coordinates of the intersection of two curves, and comparing the integral value within each node to form three judgment conditions. Finally, the data satisfying these conditions is compressed by BAQ, otherwise compressed by DPCM. Experimental results indicate that the proposed scheme improves the performance compared with BAQ method.