Adaptive modeling of sky for video processing and coding applications

Video content analysis for still- and moving images can be used for various applications, such as high-level semantic-driven operations or pixel-level contentdependent image manipulation. Within video content analysis, sky regions of an image form visually important objects, for which interesting applications at both mentioned levels can be envisaged. This paper introduces a new algorithm and model for detecting blue-sky areas, with suitable properties for TV video material. The proposed method is capable of robustly detecting various sky appearances, while addressing the requirements of the target applications. A special feature of our proposal is that we use adaptive color 1 - and position models for computing a pixel-accurate sky-probability measure. The experimental simulations show that our proposal considerably improves the correct detection/ rejection of sky regions, and yields better spatial and temporal consistency of the detection results with respect to the currently available systems

Blue Sky Detection for Content-based Television Picture Quality Enhancement

Using high-resolution flat panel displays for TV systems, the visibility of signal impairments has grown. As a solution, content-based image processing can be used for obtaining higher levels of picture-quality improvement, thereby outperforming traditional TV image-enhancement methods. This paper presents a new algorithm and feature model for blue sky detection, which enables content-adaptive enhancement of TV video sequences. The algorithm analyzes the image, creates adaptive position and color models, and classifies the sky areas of the image using a pixel-accurate soft segmentation. Such a probabilistic measure matches well with the requirements of typical video enhancement functions in TVs. We have evaluated the proposal for typical and critical natural scenes, and obtained a clear improvement over state-of-the-art techniques

Content-adaptive Image Enhancement, Based on Sky and Grass Segmentation

Current TV image enhancement functions employ globally controlled settings. A more flexible system can be achieved if the global control is extended to incorporate semantic-level image content information. In this paper, we present a system that extends existing TV image enhancement functions with control signals that are adaptive to grass and sky areas in the image. We investigate whether this content-adaptive control can offer higher levels of enhancement compared to the current global control approach. Our simulations on 117 natural images show that the mean square pixel value difference from optimally enhanced images reduces with 60%, when content-adaptive control is employed instead of a global controlled approach

Real-time FPGA-implementation for blue-sky Detection

Currently, television sets with flat plasma and LCD screens with improved resolutions and better color quality are emerging. To fully utilize their capabilities, lower resolution standard definition video material is enhanced. During such process, existing noise can become clearly visible, or additional artifacts may be introduced. These impairments are usually better visible in smooth image areas such as sky regions, motivating the development of special techniques for their removal. In this paper, we introduce a hardware accelerator for an existing pixel-accurate and spatially-consistent sky-detection algorithm. We describe the algorithmic and architectural design considerations of a resource-efficient real-time system, targeting an FPGA platform. Our results show that it is feasible to implement a simplified algorithm version by using only 5,756 logic-and 23,687 memory elements of the targeted device. A demonstrator setup using real-time camera signal, proves that images of up to 640times480 at a frame rate of 30 fps can be processed. Furthermore, according to our estimations, images with pixel rates up to 142 MHz, e.g. high definition TV, can be processed by the proposed system

Real-time FPGA-implementation for blue-sky Detection

Currently, television sets with flat plasma and LCD screens with improved resolutions and better color quality are emerging. To fully utilize their capabilities, lower resolution standard definition video material is enhanced. During such process, existing noise can become clearly visible, or additional artifacts may be introduced. These impairments are usually better visible in smooth image areas such as sky regions, motivating the development of special techniques for their removal. In this paper, we introduce a hardware accelerator for an existing pixel-accurate and spatially-consistent sky-detection algorithm. We describe the algorithmic and architectural design considerations of a resource-efficient real-time system, targeting an FPGA platform. Our results show that it is feasible to implement a simplified algorithm version by using only 5,756 logic-and 23,687 memory elements of the targeted device. A demonstrator setup using real-time camera signal, proves that images of up to 640times480 at a frame rate of 30 fps can be processed. Furthermore, according to our estimations, images with pixel rates up to 142 MHz, e.g. high definition TV, can be processed by the proposed system

Grass field detection for TV picture quality enhancement

Current TV image enhancement can be improved if the image is analyzed, objects of interest are segmented, and each segment is processed with content-specific enhancement algorithms. In this paper we present an algorithm for segmenting grass areas in video sequences. The system employs multi-scale texture analysis and adaptive color and position models for computing a pixel-based soft segmentation map. Compared to previously reported algorithms, our system shows a clear improvement in the detection result: at 10% false positive rate, the true positive rate of our algorithm yields 91%, vs. 66% and 58% of two existing methods

Grass Detection for Picture Quality Enhancement of TV Video

Current image enhancement in televisions can be improved if the image is analyzed, objects of interest are segmented, and each segment is processed with specifically optimized algorithms. In this paper we present an algorithm and feature model for segmenting grass areas in video sequences. The system employs adaptive color and position models for creating a coherent grass segmentation map. Compared with previously reported algorithms, our system shows significant improvements in spatial and temporal consistency of the results. This property makes the proposed system suitable for TV video applications

Instantaneously responsive subtitle localization and classification for TV applications

This paper presents an algorithm for localization and classification of subtitles in TV videos. We extend an existing static-region detector with object-based adaptive temporal filtering, bounding box computation around blobs of refined static regions, bounding box categorization based on geometry and filling degree of static regions, and subtitle classification using text-stroke alignment features. On a test set of more than 5000 video frames, a Precision rate of 96% is achieved at 98% Recall rate. The system detects subtitles without frame delays, and uses techniques suitable for implementation in a TV platform. We also experimentally show that the picture quality of Motion-Compensated Picture Rate Conversion in televisions can benefit from our system