KEYWORDS: Zoom lenses, Detection and tracking algorithms, Cameras, Atrial fibrillation, Digital signal processing, Digital cameras, Video, Automatic tracking, Image processing, Head
Consumer demand for fast and accurate zoom tracking has increased in the Digital Still Camera (DSC) market. Consumers desire a DSC that automatically performs zoom tracking in order to maintain the image sharpness when the zoom lens is moved towards wide-angle or tele-angle directions. Zoom tracking involves the estimation of the in-focus motor position over all zooms based on a current in-focus position before the zoom lens is moved in either direction. Normally, a zoom tracking curve is utilized to automatically track the focus motor position when the zoom lens is moved. This paper discusses and compares the real-time implementation of two widely used zoom tracking algorithms, namely geometric zoom tracking (GZT) and adaptive zoom tracking (AZT), on the Texas Instruments (TI) digital media (DM) processor. This processor is a highly integrated, programmable dual-core processor manufactured by TI specifically for the DSC market. Our previously developed rule-based search algorithm is used to perform auto-focusing over the vicinity of the tracked focus motor position when the zoom lens is halted. This is done to regain any loss in accuracy during zoom tracking. Extensive testing was carried out to examine the performance of these algorithms in terms of tracking accuracy and speed. The results show that AZT generates a better tracking accuracy while GZT provides a faster tracking speed.
This paper discusses the real-time implementation of a fast and accurate auto-focus method on the Texas Instruments DM270, a programmable processor designed specifically for digital still cameras. The DM270's programmable auto-focus hardware filter is utilized to obtain a sharpness function from a captured image. This function is then used to drive a rule-based search algorithm, which varies the focusing step size depending on the slope of the sharpness function. This leads to faster focusing speeds as compared to the standard global search algorithm. A wide variety of filters are tested by examining their performances in terms of focusing accuracy. The results show that the filters approximating the first derivative operator generate the best focusing accuracy under various focusing conditions.
In this paper, we consider the problem of rate control for video transmission. We focus on finding off-line optimal rate control for constant bit-rate (CBR) transmission, where the size of the encoder buffer and the channel rate are the constraints. To ensure a maximum minimum quality is obtained over all data units (e.g., macro blocks, video frames or group-of-pictures), we use a minimum maximum distortion (MMAX) criterion for this buffer-constrained problem. We show that, due to the buffer constraints, a MMAX solution leads to a relatively low average distortion, because the total rate budget is not completely utilized. Therefore, after finding a MMAX solution, an additional minimization of average distortion criterion is proposed to increase overall quality of the data sequence by using remaining resources. The proposed algorithm (denoted MMAX+ as it incorporates both MMAX and the additional average quality optimization stage) leads to an increase in average quality with respect to the MMAX solution, while providing a much more constant quality than MMSE solutions. Moreover we show how the MMAX+ approach can be implemented with low complexity.
We address the problem of online rate control in digital cameras, where the goal is to achieve near-constant distortion for each image. Digital cameras usually have a pre-determined number of images that can be stored for the given memory size and require limited time delay and constant quality for each image. Due to time delay restrictions, each image should be stored before the next image is received. Therefore, we need to define an online rate control that is based on the amount of memory used by previously stored images, the current image, and the estimated rate of future images. In this paper, we propose an algorithm for online rate control, in which an adaptive reference, a 'buffer-like' constraint, and a minimax criterion (as a distortion metric to achieve near-constant quality) are used. The adaptive reference is used to estimate future images and the 'buffer-like' constraint is required to keep enough memory for future images. We show that using our algorithm to select online bit allocation for each image in a randomly given set of images provides near constant quality. Also, we show that our result is near optimal when a minimax criterion is used, i.e., it achieves a performance close to that obtained by applying an off-line rate control that assumes exact knowledge of the images. Suboptimal behavior is only observed in situations where the distribution of images is not truly random (e.g., if most of the 'complex' images are captured at the end of the sequence.) Finally, we propose a T- step delay rate control algorithm and using the result of 1- step delay rate control algorithm, we show that this algorithm removes the suboptimal behavior.
Conference Committee Involvement (2)
Real-Time Image Processing 2007
29 January 2007 | San Jose, CA, United States
Real-Time Image Processing III
16 January 2006 | San Jose, California, United States
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