Takao Nishitani

A 32 kb/s toll quality ADPCM codec using a single chip signal processor

An ADPCM codec, that can provide toll quality speech at a 32 kb/s transmission rate, has been implemented on a single chip signal processor. Maximum effort has been paid to design a robust adaptation scheme for a quantizer and a predictor to withstand transmission bit errors. The codec employs a simplified robust quantizer and also employs a new backward adaptive predictor. The decoder, including the new adaptive predictor, has a structure having fixed poles and adaptive zeros, attaining both high prediction capability and robustness. The performance of a developed codec, which has analog interface capability through a PCM codec chip, satisfies the standard 64 kb/s PCM performance specification in CCITT recommendation G.712.

LSI signal processor development for communications equipment

A single chip, versatile digital signal processor has been developed, which enables compact and low cost telecommunications equipment realization. The processor architecture has been optimized for real time voiceband signal processing by adopting efficient micro-program control and by integrating a 16 × 16 bit parallel multiplier on a single chip. The processor is fabricated with N-channel MOS technology, and operates at a clock speed up to more than 8 MHz. Any instruction, including multiplication, can be performed within a 250 nanosecond interval. The paper also describes applications to actual voiceband communications equipment to prove the processor effectiveness.

A precise and stable foreground segmentation using fine-to-coarse approach in transform domain

This paper describes a precise and stable foreground segmentation using computationally efficient fine-to-coarse strategy based on a Gaussian mixture model (GMM). In our algorithm, a set of GMMs is employed on multiple block sizes by using Walsh transform (WT). Four neighboring WTs can be easily merged into a WT of four times wider block without using the inverse transform. The precise and stable processing comes from the multiresolutional GMM, and the WT spectral nature drastically reduces the computational steps. Experimental results show that our approach gives stable performance in many conditions, such as scenery in heavy snow and global lighting changes.

GMM foreground segmentation processor based on address free pixel streams

A compact implementation of a foreground segmentation processor in a multi-resolution transform domain has been proposed for HDTV signals. The proposed architecture is designed to simplify system controls by the hardware streaming and to reduce required memory capacities. It enables flowing pixels through all functional units in order, including multi-resolution spatial transform and temporal segmentation. The resultant architecture does not use memories except I/O buffers. Therefore, memory modules as well as complex address manipulation over the multiple global transforms and spatial/temporal interface is not required. The FPGA prototype chip dissipates 150 mW of power. This approach can be used for tablets and smart-phone by an ASIC implementation which will reduce the operation power to about 1/6.

Efficient multi-scale retinex algorithm using multi-rate image processing

This paper describes an efficient image enhancement method based on the Multi-Scale Retinex (MSR) theory for applying to a motion picture pre-processing. The proposed method uses a multi-rate x-y separable Gaussian filter with a polyphase structure and conducts illumination domain MSR composition. Therefore, The processing amount is drastically reduced to 4-order less than that of original MSR. As enhancing dark and bright areas in an image is the main pre-processing purpose, the method modifies an enhancement function so as to improve color rendition of bright areas on an image. Although our approach aims at simple implementation, the proposed method shows similar performance to the original one and better performance than those of histogram equalization approaches.

The study on shadow removal on transform domain GMM foreground segmentation

This paper describes a novel shadow removal method for computationally efficient transform domain foreground segmentation. As the foreground segmentation is carried out by using Walsh Transform domain, DC component as well as AC components in a transform block can be easily used for shadow removal. The DC component shows average brightness, but the AC components are related to texture of the block. A novel shadow removal uses the fact that texture of the shadow is unchanged and this leads to a simple approach to reduce shadow areas from the detected foreground objects. The experimental results show that the approach clearly eliminates shadow areas.

Wearable finger motion input interface system with GMM foreground segmentation

A wearable fingertip based input interface software has been developed. High speed detection of fingertip position for maintaining real-time software capability, stable finger motion analysis as well as reliable direction pointing have been added to the real-time JAVA based foreground segmentation software. The developed interface system can be run on a single microprocessor with a glasses-type single camera.

Software implementation approach for fingertip detection based on color multi-layer GMM

Prototype fingertip detection software has been developed for see-thru type head mount displays. The employed algorithm is newly introduced multi-resolution GMM (Gaussian Mixture model) foreground segmentation for fingertip extraction. The fingertip detection is realized on successive foreground/background binary images. The algorithm is extended to remove reflection areas as well as those of shadows by a nearby room light for indoor scenery. The employed algorithm can understand smart phone like control motions such as clicks and scrolls.

No-reference quality estimation for compressed videos based on inter-frame activity difference

This paper presents a no-reference (NR) based video-quality estimation method for compressed videos. The proposed method does not need bitstream information. Only pixel information is used for the quality estimation. An activity value which indicates a variance of luminance values is calculated for every given-size pixel block. The activity difference between an intra-coded frame and its adjacent frame is employed. In addition, blockiness and blur levels are also estimated at every frame and are taken into account. Experimental results show that the proposed method achieves accurate video-quality estimation. The correlation coefficient between subjective quality and estimated quality is 0.925. The proposed method is suitable for automatic quality check when the original videos cannot be used.

Low complexity shadow removal on foreground segmentation

A simplified shadow removal approach by using interim results of transformed domain GMM foreground segmentation has been developed. The approach is based on the fact that the spatial frequency distribution does not change from the backgrounds in the shadow areas. Due to employing gray level picture processing and to utilizing only low frequency components in the transform domain, the resultant shadow removal approach drastically reduces the amount of processing, compared to conventional shadow removal approaches based on pixel based color component processing.