Zhihai He

A unified rate-distortion analysis framework for transform coding

In our previous work, we have developed a rate-distortion (R-D) modeling framework for H.263 video coding by introducing the new concepts of characteristic rate curves and rate curve decomposition. In this paper, we further show it is a unified R-D analysis framework for all typical image/video transform coding systems, such as embedded zero-tree wavelet (EZW), set partitioning in hierarchical trees (SPIHT) and JPEG image coding; MPEG-2, H.263, and MPEG-4 video coding. Based on this framework, a unified R-D estimation and control algorithm is proposed for all typical transform coding systems. We have also provided a theoretical justification for the unique properties of the characteristic rate curves. A linear rate regulation scheme is designed to further improve the estimation accuracy and robustness, as well as to reduce the computational complexity of the R-D estimation algorithm. Our extensive experimental results show that with the proposed algorithm, we can accurately estimate the R-D functions and robustly control the output bit rate or picture quality of the image/video encoder.

Optimum bit allocation and accurate rate control for video coding via /spl rho/-domain source modeling

We present a new framework for rate-distortion (R-D) analysis, where the coding rate R and distortion D are considered as functions of /spl rho/ which is the percentage of zeros among the quantized transform coefficients. Previously (see He, Z. et al., Int. Conf. Acoustics, Speech and Sig. Proc., 2001), we observed that, in transform coding of images and videos, the rate function R(/spl rho/) is approximately linear. Based on this linear rate model, a simple and unified rate control algorithm was proposed for all standard video coding systems, such as MPEG-2, H.263, and MPEG-4. We further develop a distortion model and an optimum bit allocation scheme in the /spl rho/ domain. This bit allocation scheme is applied to MPEG-4 video coding to allocate the available bits among different video objects. The bits target of each object is then achieved by our /spl rho/-domain rate control algorithm. When coupled with a macroblock classification scheme, the above bit allocation and rate control scheme can also be applied to other video coding systems, such as H.263, at the macroblock level. Our extensive experimental results show that the proposed algorithm controls the encoder bit rate very accurately and improves the video quality significantly (by up to 1.5 dB).

Low-delay rate control for DCT video coding via /spl rho/-domain source modeling

By introducing the new concepts of characteristic rate curves and rate curve decomposition, a generic source-modeling framework is developed for transform coding of videos. Based on this framework, the rate-quantization (R-Q) and distortion-quantization (D-Q) functions (collectively called R-D functions in this work) of the video encoder can be accurately estimated with very low computational complexity before quantization and coding. With the accurate estimation of the R-Q function, a frame-level rate control algorithm is proposed for DCT video coding. The proposed algorithm outperforms the TMN8 rate control algorithm by providing more accurate and robust rate regulation and better picture quality. Based on the estimated R-D functions, an encoder-based rate-shape-smoothing algorithm is proposed. With this smoothing algorithm, the output bit stream of the encoder has both a smoothed rate shape and a consistent picture quality, which are highly desirable in practical video coding and transmission.

A linear source model and a unified rate control algorithm for DCT video coding

We show that, in any typical transform coding system, there is always a linear relationship between the coding bit rate R and the percentage of zeros among the quantized transform coefficients, denoted by /spl rho/. Based on Shannons source coding theorem, a theoretical justification is provided for this linear source model. The physical meaning of the model parameter is also discussed. We show that it is directly related to the image content and is a measure of picture complexity. In video coding, we propose an adaptive estimation scheme to estimate this model parameter. Based on the linear source model and the adaptive estimation scheme, a unified rate control algorithm is proposed for various standard video coding systems, such as MPEG-2, H.263, and MPEG-4. Our extensive simulation results show that the proposed rate control outperforms other algorithms reported in the literature by providing much more accurate and robust rate control.

From rate-distortion analysis to resource-distortion analysis

The ultimate goal in communication system design is to control and optimize the system performance under resource constraints. As the communication paradigm evolves from the conventional desktop computing, wired, and centralized communication to current mobile, wireless, distributed, and massive communication, video encoding and transmission operate under more and more resource constraints. In traditional video communication applications, such as digital TV broadcast, the major constraint is in the form of transmission bandwidth or storage space, which determines the encoding bit rate. Rate-distortion (R-D) theories have been developed to model the relationship between the coding bit rate and signal distortion. For video communication over mobile devices, the video encoding and transmission operate under additional resource constraints, such as energy supply and on-board computation capability. Therefore, there is a need to extend the traditional R-D analysis to resource-distortion analysis by incorporating the new resource constraints into the R-D analysis framework. In distributed and massive wireless video sensor networks, the resource utilization behaviors of individual video sensors should be well-coordinated through network-level rate allocation and optimum routing so as to maximize the overall performance. In this paper, we start from the classical R-D theory developed by Shannon over 50 years ago, and then review the R-D modelling techniques for modern image and video compression systems. We study the resource-distortion analysis framework for video communication over wireless devices. As one step further, we present the research problem of resource allocation and performance optimization for video compression and communication over a network of wireless communication devices.

A novel linear source model and a unified rate control algorithm for H.263/MPEG-2/MPEG-4

Let /spl rho/ be the percentage of zeros among the quantized transform coefficients. We discover that, in any typical video coding system, there is always a strictly linear relationship between /spl rho/ and the actual coding bit rate R. This linearity leads to a novel and unified source model for different types of source data and different coding systems, such as H.263, MPEG-2, and MPEG-4. The proposed linear source model is much simpler, but much more accurate than other source models reported in the literature. Based on this source model, a unified rate control algorithm is proposed for the above three video coding systems. Despite its extreme simplicity, the proposed algorithm outperforms other rate control algorithms by providing more accurate and robust rate control.

/spl rho/-domain bit allocation and rate control for real time video coding

A novel framework for rate-distortion (RD) analysis is developed. Based on this framework, an optimum bit allocation scheme and an accurate rate control algorithm are proposed for real-time video coding with H.263+ (see Cote, G. et al., IEEE Trans. on Circuits and Systems for Video Technology, vol.8, p.849-66, 1998). With the proposed algorithm, the picture quality is significantly improved. The output bit rate of the video encoder is controlled robustly and accurately according to the network condition.

Object-level bit allocation and scalable rate control for MPEG-4 video coding

In this work, a novel framework for rate-distortion (R-D) analysis is developed. Based on this framework, an object-level bit allocation scheme is proposed for MPEG-4 video coding to determine the target number of bits for each video object. We then propose a macroblock-level rate control algorithm to achieve the bits target for each video object. The proposed bit allocation and rate control algorithm outperforms the VM8 rate control algorithm by providing more accurate rate regulation and significantly improved picture quality.

/spl rho/-domain source modeling and rate control for video coding and transmission

The coding bit rate, R, is considered as a function of /spl rho/ which is the percentage of zeros among the quantized DCT (discrete cosine transform) coefficients. We discover that the rate function, R(/spl rho/), has some very interesting properties in the /spl rho/-domain. By introducing the new concepts of characteristic rate curves and rate curve decomposition, a novel framework for source modeling is proposed. Using the proposed source model, we can estimate the rate-quantization (R-Q) curve before quantization and coding with relative error less than 5%. Based on the estimated R-Q curve, the output bit rate of the video encoder can be accurately controlled. Our extensive simulation results show that the proposed algorithm outperforms TMN8 (see Ribas-Corbera, J. and Lei, S., IEEE Trans. on Circuits and Systems for Video Technology, vol.9, p.172-85, 1999) and VM7 (see Chiang, T. and Zhang, Y.-Q., IEEE Trans. on Circuits and Systems for Video Technology, vol.7, p.246-50, 1997) rate control algorithms by providing more accurate and robust rate control.

Simple and efficient wavelet image compression

We propose a simple but efficient wavelet image compression algorithm. The proposed coding scheme employs the multi-level dyadic wavelet decomposition, linear quantization with a proper dead zone, 1-D addressing complexity by raster scanning within subbands, variable length block coding, small alphabet representation of 1-D integer sequences, and adaptive arithmetic entropy coding. Despite the simplicity of the proposed coding scheme, the rate-distortion performance of the proposed image compression algorithm is competitive with the best image coders in the literature.