An Optimized Digital Watermarking Scheme Based on Invariant DC Coefficients in Spatial Domain

Digital watermarking has become an essential and important tool for copyright protection, authentication, and security of multimedia contents. It is the process of embedding a watermark in the multimedia content and its extraction. Block-based discrete cosine transform (DCT) is a widely used method in digital watermarking. This paper proposes a novel blind image watermarking scheme developed in the spatial domain by quantization of invariant direct current (DC) coefficients. The cover image is redistributed and divided into non-overlapped square blocks and then the DC coefficients invariant to rotation, row and column flip operations, without utilization of the DCT transform, are directly calculated in the spatial domain. Utilizing the quantization parameter and watermark information, the modified DC coefficients and the difference between DC and modified DC coefficients are calculated to directly modify the pixel values to embed watermark bits in the spatial domain instead of the DCT domain. Optimal values of the quantization parameter, which plays a significant role in controlling the tradeoff between robustness and invisibility, are calculated through differential evolution (DE), the optimization algorithm. Experimental results, compared with the latest similar schemes, demonstrate the advantages of the proposed scheme.

Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding

Visual surveillance systems have been playing a vital role in human modern life with a large number of applications, ranging from remote home management, public security to traffic monitoring. The recent High Efficiency Video Coding (HEVC) scalable extension, namely SHVC, provides not only the compression efficiency but also the adaptive streaming capability. However, SHVC is originally designed for videos captured from generic scenes rather than from visual surveillance systems. In this paper, we propose a novel HEVC based surveillance scalable video coding (SSVC) framework. First, to achieve high quality inter prediction, we propose a long-term reference coding method, which adaptively exploits the temporal correlation among frames in surveillance video. Second, to optimize the SSVC compression performance, we design a quantization parameter adaptation mechanism in which the relationship between SSVC rate-distortion (RD) performance and the quantization parameter is statistically modeled by a fourth-order polynomial function. Afterwards, an appropriate quantization parameter is derived for frames at long-term reference position. Experiments conducted for a common set of surveillance videos have shown that the proposed SSVC significantly outperforms the relevant SHVC standard, notably by around 6.9% and 12.6% bitrate saving for the low delay (LD) and random access (RA) coding configurations, respectively while still providing a similar perceptual decoded frame quality.