Ngai-Man Cheung

Configurable variable length code for video coding

The variable length code (VLC) tables in MPEG-1/2/4 and H.263 are fixed and optimized for a limited range of bit-rates, and they cannot handle a variety of applications. The universal variable length code (UVLC) is a new scheme to encode syntax elements and has some configurable capabilities. It is also being considered in ITU-T H.26L. However, the configurable feature of the UVLC has not been well explored. We propose configuring the UVLC with the additional code configuration (ACC). The ACC is used to adapt UVLC to different symbol distributions by adjusting the partitioning of the symbols into different categories, and the code size assignment to different categories. Experimental results show that the UVLC with ACC outperforms the current proposed scheme in H.26L and the VLC tables of existing standards, while drastically simplifying the encoding and decoding process, and is applicable to a variety of applications.

Configurable variable length code with genetic algorithms

The variable length code (VLC) tables in MPEG-1/2/4 and H.26X are fixed and optimized for a limited range of bit-rates, and they cannot handle a variety of applications. The universal variable length code (UVLC) is a new scheme to encode syntax elements and has some configurable capabilities. It is being considered in the ITU-T H.26L. However, the configurable feature of UVLC has not been well explored. In this paper we describe a VLC scheme which uses configurations as parameters to adapt UVLC to different symbol distributions of different applications. We also propose a method to automatically determine the configuration parameters based on a genetic algorithm (GA). Experimental results show that our method can achieve very good coding efficiency while drastically simplifying the encoding and decoding process, and is applicable to a variety of applications.

Head-related transfer function modeling in 3-D sound systems with genetic algorithms

Head-related transfer functions (HRTFs) describe the spectral filtering that occurs between a source sound and the listeners eardrum. Since HRTFs vary as a function of the relative source location and subject, practical implementation of 3D audio must take into account a large set of HRTFs for different azimuths and elevations. Previous work has proposed several HRTF models for data reduction. This paper describes our work in applying genetic algorithms to find a set of HRTF basis spectra, and the normal equation method to compute the optimal combination of linear weights to represent the individual HRTFs at different azimuths and elevations. The genetic algorithm selects the basis spectra from the set of original HRTF amplitude responses, using an average relative spectral error as the fitness function. Encouraging results from the experiments suggest that genetic algorithms provide an effective approach to this data reduction problem.

Software MPEG-2 video decoder on a DSP Enhanced Memory Module

This paper describes our effort in implementing software MPEG-2 video decoder using TI C6201 DSP and Basava Technology. The hardware device, DSP Enhanced Memory Module (DSP-MM), leverages the advantages of high computation performance from C6201 DSP, as well as high bandwidth memory access and efficient memory usage from Basava Technology. A prototype with C6201 DSP and 32MB SDRAM shared memory is functioning in PC environment running Windows 95 operating system. We will describe the video decoder in detail.

Genetic algorithm approach to head-related transfer functions modeling in 3-D sound system

Head-related transfer functions, or HRTFs, refer to the spectral filtering from sound sources to listeners eardrums. It is an important cue to spatial hearing. Since HRTFs vary as a function of relative source locations and subjects, practical implementation of 3D audio always races a large set of HRTFs for different azimuths and elevations (even if non-individualized HRTFs are used). Previous works have proposed several models to represent the HRTFs in order to achieve data reduction. In this paper, we describe our work In applying Genetic Algorithm (GA) to HRTF modeling. Based on a linear combination model, our system uses a GA to find the basis spectra and the normal equation method to compute the weights matrix. Individual HRTFs at different azimuths and elevations are represented as a weighted combination of the basis spectra. A set of HRTFs from the MIT Media Labs KEMAR measurements was used as the source data.