Fine Granularity Scalable Coding Research Articles

Efficient Fine-Granular Scalable Coding of 3D Mesh Sequences

An efficient fine-granular scalable coding algorithm of 3-D mesh sequences for low-latency streaming applications is proposed in this work. First, we decompose a mesh sequence into spatial and temporal layers to support scalable decoding. To support the finest-granular spatial scalability, we decimate only a single vertex at each layer to obtain the next layer. Then, we predict the coordinates of decimated vertices spatially and temporally based on a hierarchical prediction structure. Last, we quantize and transmit the spatio-temporal prediction residuals using an arithmetic coder. We propose an efficient context model for the arithmetic coding. Experiment results show that the proposed algorithm provides significantly better compression performance than the conventional algorithms, while supporting finer-granular spatial scalability.

Adaptive bitstream switching of scalable video

With scalable video coding that provides fine-granular quality degradation, such as fine granularity scalability (FGS) and progressive FGS (PFGS), or H.264 scalable video coding's (SVC) adaptive reference FGS (AR-FGS) coding, video can flexibly be streamed to receivers of heterogeneous bandwidths. However, the transmitted video is only efficiently encoded when the transmission bit rate is in the vicinity of the encoding bit rate. In this paper, we develop and evaluate a comprehensive suite of network-aware adaptive bitstream switching policies for point-to-point and point-to-multipoint streaming of fine granular scalable coded video to address this coding efficiency issue. Our approach stores a small number of encodings (versions) with different encoding bit rates for each video sequence and estimates the reconstructed quality using the motion activity levels of the underlying visual content (or, in general, any content descriptor(s) that highly correlate with the reconstructed quality). For unicast streaming, we then: (i) adaptively switch between the different encodings at the server, to improve the reconstructed video quality and (ii) adaptively drop packets during network congestion to ensure fairness between multiple unicast streams. For multicast streaming, we also adaptively switch between the different encodings to maximize the average video quality. Our adaptive bitstream switching policies consider the visual content descriptors as well as the network channel variability, while requiring only sample points from the rate-distortion curve of the video stream. From our extensive simulations with PFGS coding, we find that our adaptive unicast bitstream switching policy achieves on average a 0.8 dB improvement over the optimal non-adaptive streaming for a diverse 200-shot sequence from Star Wars IV. We have also verified our key findings with the latest scalable video coding standard, H.264 SVC.

Interactive Content-aware Video Streaming System with Fine Granularity Scalability

Video streaming with varying transmission bandwidth is becoming increasingly important. In this paper, an interactive video streaming system is proposed. Fine Granularity Scalability (FGS) is applied to be the streaming video format. The computational complexity of FGS coding is analyzed to explore an efficient FGS implementation. A new transmission model is proposed for the realization of a content-aware video streaming. At encoder side, the current MPEG-4 FGS coding flow is reordered such that the picture-level maximum can be acquired in advance and bit-plane data can be dynamically adapted. With these proposed hardware-oriented optimization approaches, a hardwired FGS block-level processing core is proposed to achieve a cost-effective solution to FGS implementation. The streaming server can adaptively decide quality-enhanced region by selective enhancement according to both object information from encoding side and user-defined region from receiver side. From the simulation results, it's demonstrated that the proposed approach can provide better quality in users' interest regions with no bit-rate or complexity overhead.

Statistical model, analysis and approximation of rate-distortion function in MPEG-4 FGS videos

In this paper, the generalized Gaussian distribution is employed first to model the DCT coefficients of image data from MPEG-4 fine-granularity scalability (FGS) frame. Then, according to the quantization theory, the distortion-rate function of the generalized Gaussian model is analyzed and it is concluded that the derivative of the distortion-rate function first decreases, and then increases up to the boundary of 6.02 as the bit rate increases. For actual FGS coding, the derivative of actual distortion-rate function usually decreases as the rate increases, and then begins to increase slowly at a comparatively high bit rate. Finally, based on above observations, a rate-distortion (R-D) model is proposed to approximate the actual distortion-rate function. Experiments show that the proposed R-D model is accurate and flexible.

Statistical Multiplexing based on MPEG-4 Fine Granularity Scalability Coding

Conventional statistical multiplexing methods for MPEG-1/2 programs involve high computationally complex transcoding (decoding and re-encoding) process to convert the original bit-rate into the target bit-rate. To avoid a time-consuming transcoding process, a new statistical multiplexer is proposed to enable content provider to easily convert the bit-rates by exploiting the MPEG-4 fine granularity scalability (FGS) coding scheme. The proposed statistical multiplexer is particularly useful for multiple-program broadcasting applications, including Internet television and video on demand, as well as value-added MPEG-4 video streaming services for DVB and ATSC digital TV systems. The proposed multiplexer mainly includes two parts: the FGS-based frame lag scheme and the optimal bit-plane truncation scheme. The FGS-based frame lag scheme exploits intra- and inter-layer correlations exist in MPEG-4 FGS bit-streams. The optimal bit-plane truncation scheme dynamically truncates enhancement layers of FGS bit-streams under the available bandwidth constraint and the quality/smoothness constraint. Experimental results show that high statistical multiplexing efficiency, inter-program fairness, and intra-program smoothness are achieved by the proposed multiplexer.

Fine granularity scalable video: implications for streaming and a trace-based evaluation methodology

Fine granularity scalability (FGS) is a new development in the area of video coding, which is designed to facilitate video streaming over communication networks. With FGS coding, the video stream can be flexibly truncated at very fine granularity to adapt to the available network resources. In this article, we introduce the communications generalist to the basic properties of FGS video coding to provide background for the design of video streaming mechanisms for FGS video. We then outline a methodology for evaluating streaming mechanisms for FGS encoded video. The methodology relies on traces of the rate-distortion characteristics of FGS encoded video and enables networking researchers and practitioners without access to video codecs and video sequences to develop and evaluate rate distortion optimized streaming mechanisms for FGS encoded video.

Fine-Grained Rate Shaping for Video Streaming over Wireless Networks

Video streaming over wireless networks faces challenges of time-varying packet loss rate and fluctuating bandwidth. In this paper, we focus on streaming precoded video that is both source and channel coded. Dynamic rate shaping has been proposed to "shape" the precompressed video to adapt to the fluctuating bandwidth. In our earlier work, rate shaping was extended to shape the channel coded precompressed video, and to take into account the time-varying packet loss rate as well as the fluctuating bandwidth of the wireless networks. However, prior work on rate shaping can only adjust the rate oarsely. In this paper, we propose "fine-grained rate shaping (FGRS)" to allow for bandwidth adaptation over a wide range of bandwidth and packet loss rate in fine granularities. The video is precoded with fine granularity scalability (FGS) followed by channel coding. Utilizing the fine granularity property of FGS and channel coding, FGRS selectively drops part of the precoded video and still yields decodable bit-stream at the decoder. Moreover, FGRS optimizes video streaming rather than achieves heuristic objectives as conventional methods. A two-stage rate-distortion (RD) optimization algorithm is proposed for FGRS. Promising results of FGRS are shown.

A fine granular joint source channel coding method

An improved FGS (Fine Granular Scalability) coding method is proposed in this letter, which is based on human visual characteristics. This method adjusts FGS coding frame rate according to the evaluation of video sequences so as to improve the coding efficiency and subject perceived quality of reconstructed images. Finally, a fine granular joint source channel coding is proposed based on the source coding method, which not only utilizes the network resources efficiently, but guarantees the reliable transmission of video information.

Block‐based fine granularity scalable video coding with optimized rate allocation for content‐aware streaming

AbstractThis article proposes a novel block‐based fine granularity scalable (FGS) video coding structure with optimized rate allocation for content‐aware streaming, which is more flexible than MPEG‐4 FGS specified in the streaming profile of MPEG‐4 standard (IS‐14496). In MPEG‐4 FGS streaming, rate allocation can only be based on frame because MBs' compressed data are interlaced between bit‐planes. However in the proposed coding structure, every MB's data are independent; therefore, streaming server can allocate bits according to content's importance, and the quality of those regions of interest is enhanced selectively with more bits allocated. In this way, the streaming server can have a differentiated delivery strategy to support a personalized streaming service considering user's preference. But the uniform rate allocation of proposed block‐based FGS coding will result in a greater loss of coding efficiency than that of MPEG‐4 FGS within a quite wide bit‐rate range. An optimized and fast rate allocation method according to block's rate‐distortion is proposed to solve this critical issue. And the coding efficiency is improved successfully, which can be comparable with MPEG‐4 FGS coding and is even better up to 0.5 dB, with some sequences at medium bit‐rates. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 322–330, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10066

Wavelet-based Fine Granularity Scalable video coding

This letter proposes an efficient wavelet-based Fine Granularity Scalable (FGS) coding scheme, where the base layer is encoded with a newly designed wavelet-based coder, and the enhancement layer is encoded with Progressive Fine Granularity Scalable (PFGS) coding. This algorithm involves multi-frame motion compensation, rate-distortion optimizing strategy with Lagrangian cost function and context-based adaptive arithmetic coding. In order to improve efficiency of the enhancement layer coding, an improved motion estimation scheme that uses both information from the base layer and the enhancement layer is also proposed in this letter. The wavelet-based coder significantly improves the coding efficiency of the base layer compared with MPEG-4 ASP (Advanced Simple Profile) and H.26L TML9. The PFGS coding is a significant improvement over MPEG-4 FGS coding at the enhancement layer. Experiments show that single layer coding efficiency gain of the proposed scheme is about 2.0–3.0dB and 0.3–1.0dB higher than that of MPEG-4 ASP and H.26L TML9, respectively. The overall coding efficiency gain of the proposed scheme is about 4.0–5.0dB higher than that of MPEG-4 FGS.