In this paper, a novel curvelet based digital image compression scheme is proposed. Aiming at achieving high compression ratio, the proposed scheme embeds a representative machine learning method, core vector machine (CVM), in the encoding process of the image compression technique. The core vector machine (CVM) has been introduced as an extremely fast classifier which is demonstrably superior to standard support vector machine (SVM) on very large datasets. In this scheme, we appropriately utilize the characteristic of CVM to reduce huge numbers of curvelet coefficients. Compared with image compression algorithms do not use CVM and methods based on wavelet transform, experimental results show that the compression performance of our method gains much improvement in peak-signal-to-noise-ratio (PSNR) and CPU time. Moreover, the algorithm works fairly well for declining block effect at higher compression ratios. 相似文献
This work presents analytical, numerical and experimental demonstrations of light diffracted through a logarithmic spiral (LS) nanoslit, which forms a type of switchable and focus‐tunable structure. Owing to a strong dependence on the incident photon spin, the proposed LS‐nanoslit converges incoming light of opposite handedness (to that of the LS‐nanoslit) into a confined subwavelength spot, while it shapes light with similar chirality into a donut‐like intensity profile. Benefitting from the varying width of the LS‐nanoslit, different incident wavelengths interfere constructively at different positions, i.e., the focal length shifts from 7.5 μm (at λ = 632.8 nm) to 10 μm (at λ = 488 nm), which opens up new opportunities for tuning and spatially separating broadband light at the micrometer scale.
