Tae Meon Bae

Scalable protection and access control in full scalable video coding

In this paper, we propose an encryption algorithm to protect scalable video coding (SVC) bitstream and a method for conditional access control to consume the encrypted SVC bitstream. To design an encryption algorithm, we analyzed the encryption requirements to support scalability function in the scalable video and proposed an effective encryption method developed in the network abstraction layer (NAL). In addition, conditional access control scheme and key management scheme are proposed to consume the SVC bitstream protected with proposed method. Experiments were performed to verify the proposed method and results showed that proposed algorithms could provide an effective access control of scalable video as well as support a video encryption with scalability function.

A selective video encryption for the region of interest in scalable video coding

In this paper, we propose an encryption method to protect the region of interest (ROI) which is standard element in the scalable video coding (SVC). The proposed video encryption method provides secured SVC contents with the selective encryption scheme. In order to confine the encryption into the ROI, three encoding schemes are also proposed, which include the constrained motion estimation, exceptional handlings for the half-pel interpolation and Intra_BL mode in the boundary of a ROI. Experiments were performed to verify the proposed methods using the joint scalable video model (JSVM), and the experimental results showed that the proposed methods protect the ROI in SVC effectively .

Video Segmentation Using Hidden Markov Model with Multimodal Features

In this paper, a video segmentation algorithm based on Hidden Markov Model classifier with multimodal feature is proposed. By using Hidden Markov Model classifier with both audio and visual features, erroneous shot boundary detection and over-segmentation were avoided compared with conventional algorithms. The experimental results show that the propose method is effective in shot detection.

Semantic event detection in structured video using hybrid HMM/SVM

In this paper, we propose a new semantic event detection algorithm in structured video. A hybrid method that combines HMM with SVM to detect semantic events in video is proposed. The proposed detection method has some advantages that it is suitable to the temporal structure of event thanks to Hidden Markov Models (HMM) and guarantees high classification accuracy thanks to Support Vector Machines (SVM). The performance of the proposed method is compared with that of HMM based method, which shows the performance increase in both recall and precision of semantic event detection.

Quality metric for H.264/AVC scalable video coding with full scalability

Scalable Video Coding (SVC) is one of the promising techniques to ensure Quality of Service (QoS) in multimedia communication through heterogeneous networks. SVC compresses a raw video into multiple bitstreams composed of a base bitstream and enhancement bitstreams to support multi scalabilities such as SNR, temporal and spatial. Therefore, it is able to extract an appropriate bitstream from original coded bitstream without re-encoding to adapt a video to user environment. In this flexible environment, QoS has appeared as an important issue for service acceptability. Therefore, there has been a need for measuring a degree of video quality to guarantee the quality of video streaming service. Existing studies on the video quality metric have mainly focused on temporal and SNR scalability. In this paper, we propose an efficient quality metric, which allows for spatial scalability as well as temporal and SNR scalability. To this end, we study the effect of frame rate, SNR, spatial scalability and motion characteristics by using the subjective quality assessment, and then a new video quality metric supporting full scalability is proposed. Experimental results show that this quality metric has high correlation with subjective quality. Because the proposed metric is able to measure a degree of video quality according to the variation of scalability, it will play an important role at the extraction point for determining the quality of SVC.

Improvement of Inter-Layer Motion Prediction in Scalable Video Coding

In this letter, we propose an enhanced method for inter-layer motion prediction in scalable video coding (SVC). For inter-layer motion prediction, the use of refined motion data in the Fine Granular Scalability (FGS) layer is proposed instead of the conventional use of motion data in the base quality layer to reduce the inter-layer redundancy efficiently. Experimental results show that the proposed method enhances coding efficiency without increasing the computational complexity of the decoder.

Watermarking system for qos aware content adaptation

Video transcoding technology is useful for seamless video service to guarantee quality of service (QoS). But video transcoding would distort the watermark signal embedded in the video content. In this paper, we propose a watermarking system that guarantees detecting watermark signal even after video transcoding. We analyzed the effect of video transcoding for watermark signal. Based on the analysis, metadata based approach is proposed to keep watermark in the video transcoding under QoS guaranteeing service environment. We performed experiments with widely used watermark algorithms to verify that the proposed watermarking system is useful.

Video genre classification using multimodal features

We propose a video genre classification method using multimodal features. The proposed method is applied for the preprocessing of automatic video summarization or the retrieval and classification of broadcasting video contents. Through a statistical analysis of low-level and middle-level audio-visual features in video, the proposed method can achieve good performance in classifying several broadcasting genres such as cartoon, drama, music video, news, and sports. In this paper, we adopt MPEG-7 audio-visual descriptors as multimodal features of video contents and evaluate the performance of the classification by feeding the features into a decision tree-based classifier which is trained by CART. The experimental results show that the proposed method can recognize several broadcasting video genres with a high accuracy and the classification performance with multimodal features is superior to the one with unimodal features in the genre classification.

Content filtering for broadcasting media

In modern digital broadcasting environment, broadcasting content filtering could provide a useful function that a TV viewer can find or store personal desired scenes from programs of multiple channels and it can be done even when one is watching a program from other channel. To achieve the filtering function in live broadcast, real-time processing is needed basically. In this paper, a broadcasting content filtering algorithm is proposed and filtering system requirements for the real-time content filtering are analyzed. To achieve real-time content processing, a buffer control algorithm is proposed as well. The usefulness of the broadcasting content filtering is demonstrated with experiments on a test-bed system.

Dynamic Full-Scalability Conversion in Scalable Video Coding

For outstanding coding efficiency with scalability functions, SVC (Scalable Video Coding) is being standardized. SVC can support spatial, temporal and SNR scalability and these scalabilities are useful to provide a smooth video streaming service even in a time varying network such as a mobile environment. But current SVC is insufficient to support dynamic video conversion with scalability, thereby the adaptation of bitrate to meet a fluctuating network condition is limited. In this paper, we propose dynamic full-scalability conversion methods for QoS adaptive video streaming in SVC. To accomplish full scalability dynamic conversion, we develop corresponding bitstream extraction, encoding and decoding schemes. At the encoder, we insert the IDR NAL periodically to solve the problems of spatial scalability conversion. At the extractor, we analyze the SVC bitstream to get the information which enable dynamic extraction. Real time extraction is achieved by using this information. Finally, we develop the decoder so that it can manage the changing scalability. Experimental results showed that dynamic full-scalability conversion was verified and it was necessary for time varying network condition.