An Efficient Codebook Initialization Approach for LBG Algorithm

In VQ based image compression technique has three major steps namely (i) Codebook Design, (ii) VQ Encoding Process and (iii) VQ Decoding Process. The performance of VQ based image compression technique depends upon the constructed codebook. A widely used technique for VQ codebook design is the Linde-Buzo-Gray (LBG) algorithm. However the performance of the standard LBG algorithm is highly dependent on the choice of the initial codebook. In this paper, we have proposed a simple and very effective approach for codebook initialization for LBG algorithm. The simulation results show that the proposed scheme is computationally efficient and gives expected performance as compared to the standard LBG algorithm

Scalable Image Retrieval by Sparse Product Quantization

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.Comment: 12 page

Improvements on stochastic vector quantization of images

A novel nonadaptive fixed-rate vector quantizer encoding scheme is presented, and preliminary results are shown. The design of the codebook has been based on a stochastic approach in order to match a previously defined model for the image to be encoded. Following this approach, the generation of the codebook is made extremely simple in terms of computational load. Good visual results are shown in the range of 0.5-0.8 bit/pixel. Much better performance is expected for adaptive schemes.Peer ReviewedPostprint (published version