41726157
Lossless compression of color mosaic images
We present a low complexity algorithm for lossless compression of color mosaic images generated by a Bayer CCD color filter array. This algorithm is based on an interesting use of the integer wavelet transform followed by a fast adaptive context-based Golomb-Rice coding. The lossless compression performance of the proposed algorithm is apparently the best reported in the literature so far for color mosaic images.
11164895
Primary-consistent soft-decision color demosaicking for digital cameras (patent pending)
Color mosaic sampling schemes are widely used in digital cameras. Given the resolution of CCD sensor arrays, the image quality of digital cameras using mosaic sampling largely depends on the performance of the color demosaicking process. A common problem with existing color demosaicking algorithms is an inconsistency of sample interpolations in different primary color channels, which is the cause of the most objectionable color artifacts. To cure the problem, we propose a new primary-consistent soft-decision framework (PCSD) of color demosaicking. In the PCSD framework, we make multiple estimates of a missing color sample under different hypotheses on edge or texture directions. The estimates are made via a primary consistent interpolation, meaning that all three primary components of a color are interpolated in the same direction. The final estimate of a color sample is obtained by testing different interpolation hypotheses in the reconstructed full-resolution color image and selecting the best via an optimal statistical decision or inference process. A concrete color demosaicking method of the PCSD framework is presented. This new method eliminates certain types of color artifacts of existing color demosaicking methods. Extensive experimental results demonstrate that the PCSD approach can significantly improve the image quality of digital cameras in both subjective and objective measures. In some instances, our gain over the competing methods can be as much as 7dB.
2090063
Primary-consistent soft-decision color demosaic for digital cameras
Bayer color mosaic sampling scheme is widely used in digital cameras. Given the resolution of CCD sensor arrays, the image quality of digital cameras using Bayer sampling mosaic largely depends on the performance of the color demosaic process. A common and serious weakness shared by all existing color demosaic algorithms is an inconsistency of sample interpolations in different primary color components, which is the culprit for the most objectionable color artifacts. To cure the problem we propose a primary-consistent color demosaic algorithm. The performance of this algorithm is further enhanced by a soft-decision sample interpolation scheme. Experiments demonstrate that the proposed framework of primary-consistent soft-decision color demosaic can significantly improve the image quality of digital cameras.
14301513
On multirate optimality of JPEG2000 code stream
Arguably, the most important and defining feature of the JPEG2000 image compression standard is its R-D optimized code stream of multiple progressive layers. This code stream is an interleaving of many scalable code streams of different sample blocks. In this paper, we reexamine the R-D optimality of JPEG2000 scalable code streams under an expected multirate distortion measure (EMRD), which is defined to be the average distortion weighted by a probability distribution of operational rates in a given range, rather than for one or few fixed rates. We prove that the JPEG2000 code stream constructed by embedded block coding of optimal truncation is almost optimal in the EMRD sense for uniform rate distribution function, even if the individual scalable code streams have nonconvex operational R-D curves. We also develop algorithms to optimize the JPEG2000 code stream for exponential and Laplacian rate distribution functions while maintaining compatibility with the JPEG2000 standard. Both of our analytical and experimental results lend strong support to JPEG2000 as a near-optimal scalable image codec in a fairly general setting.
15693719
Context-based Arithmetic Coding Reexamined for DCT Video Compression
This paper presents a new context modeling technique for arithmetic coding of DCT coefficients in video compression. A key feature of the new technique is the inclusion of all previously coded coefficient magnitudes in a DCT block in context modeling. This enables adaptive arithmetic coding to exploit the redundancy of the high-order Markov process in the DCT domain with a few conditioning states. In addition, a context weighting technique is used to further improve the coding efficiency. The complexity of the new arithmetic coding scheme is slightly lower than that of context-based adaptive binary arithmetic coding (CABAC) of H.264. Moreover, the scheme is made compatible to the AVS baseline profile. It achieves on average 13% improvement in compression ratio over context-based two dimension variable length coding (C2DVLC) designed for the DCT domain, and a similar coding efficiency as the CABAC technique in H.264.
5737792
Generators Bidding Behavior in the NYISO Day-Ahead Wholesale Electricity Market
Statistical and econometric modeling is used to analyze the dynamics of generators' bidding behavior and resulting market prices in the NYISO day-ahead market during the summer of 2001 at three levels. First, at the generator level, generators' offered capacity is modeled by a two-stage sample selection process. The grouping choices of generators are estimated in the first stage. In the second stage, generators' offered capacity is estimated conditional on their grouping choices. Second, at the group level, we estimate the offer curves of each group. Third, at the total supply level, the aggregate supply curve is simulated by aggregating the offer curves of bidding groups. The estimated market prices based on the simulated supply curve can replicate most volatility of actual market prices. We find that demand conditions affect generators' bidding behavior and thus market prices significantly by applying our model under different demand assumptions
18850818
A Fast Algorithm for the Chirp Rate Estimation
A fast algorithm for the chirp rate estimation is presented in this paper. By the computation of the cubic phase transform (CPT) of the signal, it is shown that the CPT is concentrated along the frequency rate law of the signal, and the peak of the CPT yields the estimate of the chirp rate. The proposed algorithm is fast in that it requires only one-dimensional (1-D) maximizations. The asymptotic statistical result is derived for the chirp rate estimation, and the computer simulation demonstrates that, when the input signal-to-noise ratio (SNR) is higher than -2 dB, the simulation results and the theoretical analyses are well matched, and the mean-square error (MSE) approaches optimality as the SNR tends to infinity.
445053
Prediction of Helix, Strand Segments from Primary Protein Sequences by a Set of Neural Networks
In prediction of secondary structure of proteins there are always some suspected segments. These suspected segments confuse people and lower the accuracy of prediction methods. To deal with this problem, a set of neural networks (NNs) are built based on helix, strand and coil segments selected from PDB. The test performance of these NNs on training data is perfect without surprise. However the prediction on test data is not good enough because the training data are lake of great representativeness. The results support the fact that closer neighbor vectors have the similar outputs of NNs. One can improve representativeness of training data without enlarging data scale as long as select less data from dense region and more from sparse region on condition that distribution of sample data has been known.
1064501
Trade-off scheme for fault tolerant connected dominating sets on size and diameter
Connected Dominating Set (CDS) has been a well known approach for constructing a virtual backbone to alleviate the broadcasting storm in wireless networks. Current research has focused on minimizing the size and diameter or improving the fault tolerance of CDS. However, to our best knowledge, no existing research has considered these three important factors together in a single model. In this paper, we introduce the fault tolerant model studying a joint optimization problem in which the objective is to minimize the CDS size as well as the network latency. This model also addresses the tradeoffs between the objective functions. We next propose one approximation algorithm and two distributed algorithms with constant ratios for the model. Simulation results show that our algorithms can gain good tradeoffs between the three factors, which coincide with theoretical analysis. Moreover, our algorithms could obtain a better performance than previous work.
36024332
L-∞ constrained micronoise filtering for high-fidelity image compression
Imaging apparatus inevitably impose undesirable noises onto acquired images during real imaging process. Usually these noises are too faint to cause unpleasing visual effects, however, they degrade image fidelity and significantly lower the compression ratio of lossless coding. More baffling, in this case, there leaves little room for traditional noise filtering methods to work. This paper will introduce some of our efforts trying to weaken the effect of such Micro Noise during near-lossless compression. Experimental results on ISO test images and micro Gaussian noises demonstrate that with potentiality of filtering micro noise, an improved near-lossless coder can not only achieve obviously higher compression ratio but also provide better image fidelity (measured by mean squared error) than lossless coding.