Measurement matrix construction is critically important to signal sampling and reconstruction for compressed sensing. From a practical point of view, deterministic construction of the measurement matrix is better than random construction. In this paper, we propose a novel deterministic method to construct a measurement matrix for compressed sensing, CS-FF (compressed sensing-finite field) algorithm. For this proposed algorithm, the constructed measurement matrix is from the finite field Quasi-cyclic Low Density Parity Check (QC-LDPC) code and thus it has quasi-cyclic structure. Furthermore, we construct three groups of measurement matrices. The first group matrices are the proposed matrix and other matrices including deterministic construction matrices and random construction matrices. The other two group matrices are both constructed by our method. We compare the recovery performance of these matrices. Simulation results demonstrate that the recovery performance of our matrix is superior to that of the other matrices. In addition, simulation results show that the compression ratio is an important parameter to analyse and predict the recovery performance of the proposed measurement matrix. Moreover, these matrices have less storage requirement than that of a random one, and they achieve a better trade-off between complexity and performance. Therefore, from practical perspective, the proposed scheme is hardware friendly and easily implemented, and it is suitable to compressed sensing for its quasi-cyclic structure and good recovery performance.

Index compression is partially responsible for the current performance achievements of Internet search engines. Among many latest compression techniques, Simple9 can pack as many integers as possible into a single 32-bit machine word using 9 different padding modes. However, the number of wasted bits in Simple9 remains large. In previous works, researchers have focused on reducing the unused trailing bits of the padding modes and have proposed various additional modes that make full use of the cases of the status bits. Instead, we focus on the wasted bits in the integer list, padding extra zeros for a complete dense mode when the number of integers is not enough to fit a complete mode. More precisely, we first propose a novel index compression method called SimpleD with dense padding modes to achieve a more compact storage compared with that of Simple9. We then design an innovative metric for extracting the inserted extra zero integers during the decoding phase. Experiments on the TREC WT2G and GOV2 datasets show that our encoder outperforms Simple9 while still retaining a very fast decompression speed.

Two schemes for fast identification of JPEG coded images are proposed in this paper. The aim is to identify the JPEG images that are generated from the same original image and have equivalent or different compression ratios. Fast identification can be achieved since the schemes work on the quantized Discrete Cosine Transform (DCT) domain. It is not required to inverse the quantization and the DCT. Moreover, only a few coefficients are commonly required for identification. The first approach can avoid identification leakage or false negative (FN), and probably result in a few false positives (FP). The second approach can avoid both FN and FP, with a slightly higher processing time. By combining the two schemes, a faster and a more perfect identification can be achieved, in which FN and FP can be avoided.