The Fine Grain Scalability (FGS) technique used in SVC codec encodes and decodes the quantization error of QBL (quality base layer) along the cyclic scanning path. The FGS technique provides the scalability property to the compressed bit stream. However, the cyclic scanning procedure of FGS method may require a huge computing time. In this paper, we propose a fast FGS decoding scheme, which has a lower decoding complexity without sacrificing image quality.

In heterogeneous network environments, it is mandatory to measure the grade of the video quality in order to guarantee the optimal quality of the video streaming service. Quality of Service (QoS) has become a key issue for service acceptability and user satisfaction. Although there have been many recent works regarding video quality, most of them have been limited to measuring quality within temporal and Signal-to-Noise Ratio (SNR) scalability. H.264/AVC Scalable Video Coding (SVC) has emerged and has been developed to support full scalability. This includes spatial, temporal, and SNR scalability, each of which shows different visual effects. The aim of this paper is to define and develop a novel video quality metric allowing full scalability. It focuses on the effect of frame rate, SNR, the change of spatial resolution, and motion characteristics using subjective quality assessment. Experimental results show the proposed quality metric has a high correlation to subjective quality and that it is useful in determining the video quality of SVC.

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.

In this paper, theoretical properties of deinterlacer banks are analyzed. Deinterlacer banks are novel filter banks in the sense that a progressive video sequence is separated into two progressive video sequences of a half frame rate and, furthermore, interlaced sequences are produced as intermediate data. Unlike the conventional filter banks, our deinterlacer banks are constructed in a way unique to multidimensional systems by using invertible deinterlacers, which the authors have proposed before. The system is a kind of shift-varying filter banks and it was impossible to derive the optimal bit-allocation control without any equivalent parallel filter banks. This paper derives an equivalent polyphase matrix representation of the whole system and its equivalent parallel structure, and then shows the optimal rate allocation for the deinterlacer banks. Some experimental results justify the effectiveness of the optimal rate allocation through our theoretical analysis.

This paper introduces an adaptive GOP structure (AGS), which adaptively defines the GOP structure according to the time-varying temporal properties of video sequences, and thus improves the coding efficiency of the MPEG & ITU-T's Joint Scalable Video Coding (JSVC) scheme, the method proposed in this paper, which adaptively modifies the size of GOP based on the image characteristics of video sequence, improves the coding efficiency up to 0.77 dB compared to the JSVC JSVM (Joint Scalable Video Model).

An H.264-based selective FGS coding scheme is proposed. It selectively uses the interframe-prediction data inside the enhancement-layer only when those data can significantly reduce the temporal-redundancies. Since this minimizes the drift effects, the overall coding efficiency is improved. Simulations show that average PSNR of the proposed scheme is higher by 1-3 dB and 3-5 dB than those of the H.264-based FGS and the MPEG-4 video FGS profile, respectively.

This paper introduces the water ring scan method especially designed for the scalable video coding schemes such as fine granularity scalabilities (FGS) on the basis of MPEG-4 part-2 and the H.264. The proposed scanning method can improve the subjective quality of the decoded video by most-preferentially encoding, transmitting and decoding the image information of the region of interest. From the various simulation results of FGS coding schemes with MPEG-4 part 2 and H.264, the proposed scanning method can improve the subjective picture quality about 0.5 dB 3.5 dB better than the widely used raster scan order, especially on the region of interest, without significant loss of the quality in the left-over region.

CDN (Content Delivery Networks) improves end-user performance by replicating web contents on a group of geographically distributed servers. However, repeatedly keeping the entire replica of the original objects into many content servers consumes too much server resource. This problem becomes more serious for the large-sized objects such as streaming media, e.g. high quality video. In this paper, we therefore propose an efficient replication method for layered video streams in CDN, which can reduce user response delays and storage costs simultaneously. Based on an analytical formulation of the cooperative replication of layers and segments of each video stream, we derive a replication algorithm which solves next three problems quantitatively. (1) How many servers should be selected to replicate a given video stream? (2) For a single video stream, how many layers and segments should be stored in a given server? (3) After selecting a group of servers for each video stream, how do we allocate the replication priority (i.e. order) to each server? Simulation results verify that the proposed algorithm efficiently resolves the above problems and provides much better performance than conventional methods.

In the past, enhancement techniques for the end-to-end quality of a networked application were studied by looking at each individual layer. Examples of such techniques include the error resilience/concealment methods in the application layer, the FEC/ARQ in the transport layer, and the Quality of Service (QoS) techniques in the network layers. However, an integrated approach that would look across all related layers had yet to be investigated. This paper proposes an approach that combines priority-aware video packetization, adaptively used layered FEC, and QoS controlled networks such as IntServ and DiffServ in order to provide an efficient end-to-end quality in layered streaming video. The combination is more efficient in terms of a simple network price mechanism, that is, in getting the best end-to-end quality under a given total cost constraint. Our proposed approach in DiffServ with video packet (VP) data-splitting and layered FEC guarantees minimal service quality in a scalable and cost effective manner without introducing resource reservation. For video, we also propose performance metrics such as corrupted/frozen frame ratio (CFR, FFR). These provide objective metrics like PSNR as well as a measurement for subjective perceptions. Our approach, which combines related layers such as video coding, transport, and network, has yielded results that have proven to be more cost-effective and practical than the supporting network QoS.