We propose a novel algorithm for color image compressed sensing (CS). Our method involves the adaptive measurement and reconstruction of color images based on visual saliency detection. First, we divide the image into blocks and transform the RGB channel into the YUV channel. Secondly, we use statistical texture distinctiveness to calculate the saliency of each block and normalize energy, thereby establishing an adaptive measurement rate and measurement matrix. Thirdly, we adaptively measure the Y channel according to block prominence and preserve the information of the UV channel. During reconstruction, we utilize adaptive block measurement rate to re-estimate block saliency and then reconstruct the objective function of the weighted reconstruction model according to the re-estimated block saliency. Finally, we combine the reconstructed Y channel with the reserved UV channel to obtain the final image. Experimental results show that compared with other state-of-the-art approaches, the proposed algorithm can not only provide good subjective visual quality but can also present higher peak signal to noise ratio (PSNR) under the same sampling rate.

Digital video is vulnerable to accidental or malicious destruction in storage, transmission, processing, which damages the legitimate rights and interests of the product owners. Digital video watermarking technology is a mean to protect intellectual property rights. We analyze the basic principle of the 3D-Harris algorithm and Gabor filter method. By considering the temporal causality of the video, the common symmetrical filter is discarded. We design a causal filter which conforms to video characteristics and proposes an asymmetric causal filter in space–time domain. On this basis, an improved video watermarking algorithm is proposed, combining space–time feature points and DCT domain. The experimental results show that proposed algorithm can not only ensure high invisibility, and can effectively resist various attacks in time domain and space domain.

In this paper, we propose a high efficiency deterministic measurement matrix for practical compressive sensing based on the combination of Logistic Chaotic system and correlation, called Chaos-Gaussian measurement matrix. Initially, deterministic Logistic system has been used to generate Chaotic sequence with good pseudo-random. Subsequently, two spread spectrum sequences have been constructed and been verified to follow Gaussian distribution. On the basis of the observation mentioned previously, Chaos-Gaussian measurement matrix is constructed. Experimental results corroborate that Chaos-Gaussian measurement matrix is superior to Gaussian and Bernoulli random measurement matrix. Furthermore, Chaos-Gaussian measurement matrix balances the randomness and the certainty.

Saliency detection has attracted significant attention in the field of computer vision technology over years. At present, more than 100 saliency detection models have been proposed. In this paper, a relatively more detailed classification is proposed. Furthermore, we selected 25 models and evaluated their performance using four public image datasets. We also discussed common problems, such as the influence to model performance by prior information and multiple objects. Finally, we offered future research directions.