Sponsored by the National Aeronautical Space Association (NASA), the Synergetic Education and Research in Enabling NASA-centered Academic Development of Engineers and Space Scientists (SERENADES) Laboratory was established at California State University, Los Angeles (CSULA). An important on-going research activity in this lab is to develop an easy-to-use image analysis software with the capability of automated object detection to facilitate astronomical research. This paper presents the design and implementation of an automated astronomical image analyzer. The core of this software is the automated object detection algorithm developed in our previous research, which is capable of detecting objects in near galaxy images, including objects located within clouds. In addition to the functionality, human factor is considered in system design and tremendous efforts have been devoted to enhance the user friendliness. Instead of using command line or static menus, graphical methods are enabled in our software system to allow the user to directly manipulate the objects that he/she wants to investigate. Comprehensive tests are conducted by users with and without astronomical backgrounds. Compared to current software tools such as IRAF and Skyview, our developed software has the following advantages: 1) No pre-training is required; 2) The amount of human supervision is significantly reduced by automated object detection; 3) Batch processing capability is supported for fast operation; and 4) A high degree of human computer interaction is realized for better usability.
The James Webb Space Telescope (JWST) is expected to produce a vast amount of images that are valuable for astronomical research and education. To support research activities related to the mission, the National Aeronautical and Space Administration (NASA) has provided funds to establish the Structures Pointing and Control Engineering (SPACE) Laboratory at the California State University, Los Angeles (CSULA). One of the research activities in SPACE lab is to design and implement an effective and efficient transmission system to disseminate JWST images across networks. In on our previous research, a prioritized transmission method was proposed to provide the best quality of the transferred image based on the joint-optimization of content-based retransmission and error concealment. In this paper, the design and implementation of a robust transmission system is presented to utilize our previously proposed methods over error-prone links. The implemented system includes three parts. First, a zero-tree based error-resilient wavelet codec is used to compress the incoming astronomical image at the sender. Tree-based interleaving is adopted in packetization to increase the system's capability to combat burst losses in error-prone channels. Second, various error concealment approaches are investigated and implemented at the receiver to improve the quality of the reconstructed image. The transmission system uses UDP as the transport protocol, but with an error control module to incorporate the optimal retransmission with the delay constraint. A user-friendly graphical interface is designed to allow easy usage for users of diverse backgrounds.
The James Webb Space Telescope (JWST) is expected to produce a vast amount of images that are valuable for astronomical research and education. To support research activities related to JWST mission, NASA has provided funds to establish the Structures Pointing and Control Engineering (SPACE) Laboratory at the California State University, Los Angeles (CSULA). One of the research activities in SPACE lab is to design an effective and efficient transmission system to disseminate JWST images across the Internet.
This paper presents a prioritized transmission method to provide the best quality of the transferred image based on the joint-optimization of content-based retransmission and error concealment. First, the astronomical image is compressed using a scalable wavelet-based approach, then packetized into independently decodable packets. To facilitate the joint-optimization of two mutually dependent error control methods, a novel content index is declared to represent the significance of the packet content as well as its importance in error concealment. Based on the defined content index, the optimal retransmission schedule is determined to maximize the quality of the received image under delay constraint with the given error concealment method. Experimental results demonstrate that the proposed approach is very effective to combat the packet loss during transmission to achieve a desirable quality of the received astronomical images.
Sponsored by the National Aeronautical Space Association (NASA), the Synergetic Education and Research in Enabling NASA-centered Academic Development of Engineers and Space Scientists (SERENADES) Laboratory was established at California State University, Los Angeles (CSULA). An important on-going research activity in this lab is to develop an easy-to-use image analysis software with the capability of automated object detection to facilitate astronomical research. This paper presented a fast object detection algorithm based on the characteristics of astronomical images. This algorithm consists of three steps. First, the foreground and background are separated using histogram-based approach. Second, connectivity analysis is conducted to extract individual object. The final step is post processing which refines the detection results. To improve the detection accuracy when some objects are blocked by clouds, top-hat transform is employed to split the sky into cloudy region and non-cloudy region. A multi-level thresholding algorithm is developed to select the optimal threshold for different regions. Experimental results show that our proposed approach can successfully detect the blocked objects by clouds.
The noise-alike nature of astronomical images imposes a great challenge on compression. Due to the lack of correlation among adjacent pixels, it is very difficult to achieve good compression result using standard algorithms. To address the above challenge, a novel object-based compression method is proposed in this paper. Based on object analysis, the astronomical entities presented in the image are classified into two categories: clear and faint objects. For the former, a zerotree based wavelet compression algorithm is employed to achieve scalable coding; for the latter, a predictive coding method is used to preserve their location and intensity. The objective is to enhance the detection of faint object in astronomical images while providing a good overall visual effect. Experiment results demonstrate the superior performance of our proposed algorithm.
In order to provide good QoS for video streaming in error-prone environments, effective error control methods are essential. Current error control methods can be classified into two categories: 1) Transport layer approaches such as FEC and retransmission; 2) Application layer approaches such as error resilience coding and error concealment. By far, most existing research is aimed towards optimizing one of the above approaches to reduce the impact of transmission errors. However, there is usually more than one error control method in a real video streaming system. In this case, how to optimize the system performance becomes more complicated, and is not standardized. This paper presents the research effort to joint-optimize the effects of two error control methods, retransmission and error concealment, in wavelet-based video streaming system. The major difficulty of the joint-optimization is that the two methods are mutually dependent; the system cannot be optimized by improving each error control method independently. To tackle this problem, a new content index, namely "reconstruction distortion", is defined to quantify both the packet content and its importance in error concealment. Based on the defined content index, a content-based retransmission approach is developed to select the best packet-sending scheme to maximize the quality of the received video under the given error concealment method. Experiments results demonstrate the effectiveness of the proposed method.
Effective and efficient video streaming has become a popular research topic in recent years. Significant research has been done on streaming techniques for MPEG video. In comparison, research on the same for wavelet-compressed video is far from adequate. In the paper, we exploit the features of the 3-D wavelet video in streaming applications to design a novel streaming framework. The new framework takes into consideration of the video content in both compression and transmission scheme for optimal performance. The framework consists of two parts: a compression module specially designed for video streaming and a robust transmission module. In the compression module, the input video is first segmented into GOFs (group of frames) by a dynamical grouping approach such that the frames of the same GOF have similar contents. Then the integer-based 3-D wavelet transform is used to achieve real-time computation. To prevent error propagation in the streaming, a bounded coding scheme is employed such that the output bitstreams of different subbands are independent. In the transmission module, a content-based retransmission scheme is adopted to minimize the distortion caused by packet loss, while subjecting to both rate and delay constraints. The optimized transmission is achieved by a fast decision-making system that employs a heuristic algorithm to drop the least significant packets if necessary to ensure the delivery of more important ones in the same GOF. Furthermore, optimal bandwidth allocation between different GOFs is implemented to keep the video quality consistent regardless of the packet loss rate. Experimental results are provided to show the effectiveness of the designed framework.