Reduced-reference (RR) image quality assessment (IQA) algorithm aims to automatically evaluate the distorted image quality with partial reference data. The goal of RR IQA metric is to achieve higher quality prediction accuracy using less reference information. In this paper, we introduce a new RR IQA metric by quantifying the difference of discrete cosine transform (DCT) entropy features between the reference and distorted images. Neurophysiological evidences indicate that the human visual system presents different sensitivities to different frequency bands. Moreover, distortions on different bands result in individual quality degradations. Therefore, we suggest to calculate the information degradation on each band separately for quality assessment. The information degradations are firstly measured by the entropy difference of reorganized DCT coefficients. Then, the entropy differences on all bands are pooled to obtain the quality score. Experimental results on LIVE, CSIQ, TID2008, Toyama and IVC databases show that the proposed method performs highly consistent with human perception with limited reference data (8 values).

In this letter, a novel steganographic method with four-pixel differencing and exploiting modification direction is proposed. Secret data are embedded into each four-pixel block by adaptively applying exploiting modification direction technique. The difference value of the four-pixel block is used to judge whether the pixels in edge areas can tolerate larger changes than those in smooth areas. The readjustment guarantees to extract the secret data exactly and to minimize the embedding distortion. Since the proposed method processes non-overlapping 22 pixels blocks instead of two consecutive pixels, the features of edge can be considered sufficiently. Compared with the previous method, experimental results show that the proposed method provides better performance, i.e., larger embedding capacity and better image quality.