One of the key procedures in color image compression is to extract its region of interests (ROIs) and evaluate different compression ratios. A new non-uniform color image compression algorithm with high efficiency is proposed in this paper by using a biology-motivated selective attention model for the effective extraction of ROIs in natural images. When the ROIs have been extracted and labeled in the image, the subsequent work is to encode the ROIs and other regions with different compression ratios via popular JPEG algorithm. Furthermore, experiment results and quantitative and qualitative analysis in the paper show perfect performance when comparing with other traditional color image compression approaches.

An airport runway centerline location method is proposed for extracting airport runway in images from one-off aerial imaging system. One-off aerial imaging system captures image at an altitude about one kilometer or below, thus detailed feature of the scenery reveals itself clearly. The proposed method relies on this precondition to detect and locate centerline of airport runway. This method has four steps: edge detection, dominating line orientation extraction, distance histogram building and centerline location. A salient edge detection method is developed with Sobel detector, which could detect edges of runway strips at the disturbance of edges features from surrounding objects. Then, a traditional Hough transform is performed to build a Hough map, within which the dominating line orientation is extracted. After getting the dominating line orientation, a reference straight line is chosen for building distance histogram. This distance histogram is a one-dimensional one, built up with the distance of all edge pixels in the edge map to the reference line. Airport centerline has a three-peak pattern in the one-dimensional distance histogram, and the center peak is corresponding to the centerline of airport runway. Experiments with simulated images show this method could location airport runway centerline effectively.

A novel unsupervised approach for regions of interest (ROI) extraction that combines the modified visual attention model and clustering analysis method is proposed. Then the non-uniform color image compression algorithm is followed to compress ROI and other regions with different compression ratios through the JPEG image compression algorithm. The reconstruction algorithm of the compressed image is similar to that of the JPEG algorithm. Experimental results show that the proposed method has better performance in terms of compression ratio and fidelity when comparing with other traditional approaches.OCIS codes: 100.0100, 100.2000100. , 150.0150, 150.1135 At present, lossy compression of still images is one of the research focuses in the digital image-processing field. Under the condition of the limited storage capacity of the imaging system and the constrained information transmission wide band, traditional uniform compression ratio (CR) algorithms are unable to consider both the requirements of high CR and image reconstruction quality. The non-uniform image compression methods based on regions of interest (ROI) apply lossless or high-fidelity compression to ROI while adopting a high-CR method to the background, which results in the effective compression of image redundancies, with important information being preserved as much as possible. Discriminating the ROI from the background is important. The most direct method to achieve this is to allow the user to select the interesting regions [1] . However, such an image-processing system may become quite complicated. Unsupervised extraction of a specific ROI from an image is an important procedure for computer vision and image-processing algorithms.Human beings have the remarkable ability to determine ROI in complex scenes quickly. This ability is called "selective visual attention mechanism" (SVAM). Therefore, introducing SVAM in unsupervised ROI extraction is important and necessary because it can reduce computational complexity [2] , save computational resources, and improve the efficiency of image processing.In this letter, a novel, non-uniform image compression approach based on an unsupervised ROI extraction algorithm is proposed. The algorithm can successfully distinguish ROI from the original image. When ROI have been extracted, the ROI and other regions are encoded with different CRs[3] using a popular image compression algorithm.The proposed approach adopts the intersection of the saliency map produced by a modified Itti-Koch visual attention model and the segmentation result of the clustering analysis algorithm to determine ROI. A flowchart of the proposed model is shown in Fig. 1.If peaks in the saliency map overlap with regions determined by the image segmentation, the ROI is extracted based on these regions. One of the most important advantages of the proposed model is that its execution is entirely unsupervised.The original Itti-Koch model of visual attention uses the dyadic Gaussian pyramid to subsample the input image [4] . In contrast, in the propo...

Realistic image rendition is to reproduce the human perception of natural scenes. Retinex is a classical algorithm that simultaneously provides high dynamic range compression contrast and color constancy of an image. In this paper, we discuss a design of a digital signal processor (DSP) implementation of the single scale monochromatic Retinex algorithm. The target processor is Texas Instruments TMS320DM642, a 32-bit fix point DSP which is clocked at 600 MHz. This DSP hardware platform designed is of powerful consumption and video image processing capability. We give an overview of the DSP hardware and software, and discuss some feasible optimizations to achieve a real-time version of the Retinex algorithm. In the end, the performance of the algorithm executing on DSP platform is shown.

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