We propose an efficient method to label the frames of a video consistently and quickly. Our method is motivated by welding videos, where molten metal moves continuously, and some frames may be noisy due to the high intensity of arc and spatters that can obscure the desired point to be annotated. The traditional annotation methods which pause the videos and annotate each frame individually are time-consuming and may result in inconsistent labeling between different annotators for noisy frames of a video. Our new proposed method benefits from the fact that video sequences are contiguous. We track the location of the cursor while the video is being played, with the user controlling the playback speed, including the playback direction. We process the recorded cursor locations to convert them to the final annotation. Compared to frame-to-frame annotation methods, we show that our proposed interface speeds up the annotating process by 21 times while maintaining consistency of the labels.

The authors address the use of sparse representation to securely hide a message within non‐overlapping blocks of a given colour image in the wavelet domain. All four sub‐images of the two‐dimensional wavelet transform of two colour bands are used for data embedding without affecting the image perceptibility. Bit error rate of hidden data extraction is reduced to zero by introducing a novel refinement procedure in the proposed algorithm. The refinement procedure introduced solves the hidden bit extraction errors caused by the rounding process, the overflows and the nature of approximation in sparse decomposition. Capacity of the proposed method is calculated using necessary conditions for uniqueness of the sparse representations and validated by means of experimental results. The authors experimental results show that the embedded data are invisible perceptually. The average peak signal‐to‐noise ratio (PSNR) value of the introduced method is about 40 and 11 dB higher than the average PSNR values of the MPSteg‐colour method and the wavelet domain 2‐LSB embedding method, respectively. The security of the proposed method is also evaluated comparatively, using five well‐known steganalysers, which confirms superior performance of the new method comparing with the MPSteg‐colour method and the wavelet domain 2‐LSB embedding method.

In this paper, we present a novel speech steganography method using discrete wavelet transform and sparse decomposition to address the undetectability concern in speech steganography. The proposed speech steganography method exploits the sparse representation to embed secret messages into higher semantic levels of the cover signal, resulting in increased undetectability. The proposed method also yields improvements on both stego signal quality and embedding capacity, which are the two major requirements of a steganography algorithm. Our experimental results illustrate that the stego signals generated by the proposed method are perceptually indistinguishable from the original cover signals, quantified by both SNR and PESQ quality measures. When compared with two well-known steganography methods, the proposed method is shown to be superior on addressing major requirements of a steganography algorithm, imperceptibility, undetectability, and capacity.