Image vector quantization codec indices recovery using Lagrange interpolation
Vector quantization (VQ) which has been widely used in the field of video and image coding is an efficient coding algorithm due to its fast decoding efficiency. Sometimes, the indices of VQ will be lost because of the signal interference during the transmission. In this paper, we propose an efficient estimation method by using the Lagrange interpolation formula to conceal and recover the lost indices on the decoder side instead of re-transmitting the whole image again. If the image or video has the limitation of the period of validity, re-transmitting the data wastes of time and occupies the network bandwidth. Therefore, using the received correct data to estimate and recover the lost data is efficient in time constraint situation such as network conference or mobile transmission. For nature images, the pixels with its neighbors are correlative and VQ partitions the image into sub-blocks and quantize them to form the indices to transmit, the correlation between adjacent indices is very strong as well. There are two parts of the proposed method. The first one is preprocessing and the other is the estimation process. In preprocessing, we modify the order of code-vectors in the VQ codebook to increases the correlation among the neighboring vectors. In the second part, using the Lagrange interpolation formula to constitute a polynomial to describe the tendency of VQ indices and use the polynomial to estimate the lost VQ indices on the decoder side. Using conventional VQ to compress Lenna and transmit without any index lost can achieve the PSNR of 30.154dB on the decoder. The simulation results demonstrate that our method can efficient estimate the indices to achieve the PSNR value of 28.418dB when the lost rate is 5% and 29.735dB at the lost rate of 1%.
vector quantization lagrange interpolation image communication index recovery