This paper proposes a fast algebraic codebook search for DSVD applications. In this method, the codebook search is simplified by reducing the number of possible position combinations using a mean-based track threshold multiplied by heuristically determined optimum threshold factor. And, to guarantee a complexity requirement of DSVD, the maximum number of searching position combinations is limited to 320. The proposed method reduced computational complexity considerably, compared with G.729 with a slight degradation of SNR. Particularly, it shows better speech quality with lower complexity than G.729A.

In this paper, a technique to reduce the overhead of Vector Quantization (VQ) coding is developed here. Our method exploits the inter-index correlation property to reduce the overhead to transmit encoded indices. Discrete Cosine Transform (DCT) is the tool to decorrelate the above correlation to get further bit rate reduction. As we know, the codewords in the codebook that generated from conventional LBG algorithm do not have any specified orders. Hence, the indices for selected codewords to represent respective adjacent blocks are random distributions. However, due to the homogeneous property existing among adjacent regions in original image, we re-arrange the codebook according to our predefined weighting criterion to enable the selected neighboring indices capable of indicating the homogeneous feature as well. Then, DCT is used to compress those VQ encoded indices. Because of the homogeneous characteristics existing among the selected adjacent indices after codebook permutation, DCT can achieve better compression efficiency. However, as we know, DCT introduces distortion by the quantization procedure, which yield error-decoded indices. Therefore, we utilize an index residue compensation method to make up that error decoded indices which have high complexity deviation to reduce those unpleasant visual effects caused by distorted indices. Statistics illustrators and table are addressed to demonstrate the efficient performance of proposed method. Experiments are carried out to Lena and other natural gray images to demonstrate our claims. Simulation results show that our method saves more than 50% bit rate to some images while preserving the same reconstructed image qualities as standard VQ coding scheme.

In this paper, we propose an adaptive encoding method of fixed codebook in CELP coders and implement an adaptive fixed code-excited linear prediction (AF-CELP) speech coder as a low-bit-rate extension to the 8 kbit/s CS-ACELP. The AF-CELP can be implemented at low bit rates as well as low complexity by exploiting the fact that the fixed codebook contribution to the speech signal is periodic, as is the adaptive codebook (or pitch filter) contribution. Listening tests show that the 6.4 kbit/s AF-CELP has a comparable quality to the 8 kbit/s CS-ACELP under real environmental test conditions.

A trained sparse conjugate codebook is proposed for improving the speech quality of CELP-based coding in a noisy environment. Although CELP coding provides high quality at a low bit rate in a silent environment (creating clean speech), it cannot provide a satisfactory quality in a noisy environment because the conventional fixed codebook is designed to be suitable for clean speech. The proposed codebook consists of two sub-codebooks; each sub-codebook consists of a random component and a trained component. Each component has excitation vectors consisting of a few pulses. In the random component, pulse position and amplitude are determined randomly. Since the radom component does not depend on the speech characteristics, it handles noise better than the trained one. The trained component maintains high quality for clean speech. Since excitation vector is the sum of the two sub-excitation vectors, this codebook handles various speech conditions by selecting a sub-vector from each component. This codebook also reduces the computational complexity of a fixed codebook search and memory requirements compared with the conventional codebook. Subjective testing (absolute category rating (ACR) and degradation category rating (DCR)) indicated that this codebook improves speech quality compared with the conventional trained codebook for noisy speech. The ACR test showed that the quality of the 8 kbit/s CELP coder with this codebook is equivalent to that of the 32 kbit/s ADPCM for clean speech.