Video Coding Method Research Articles

Coverless Video Steganography Based on Frame Sequence Perceptual Distance Mapping

Most existing coverless video steganography algorithms use a particular video frame for information hiding. These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness. We propose a coverless video steganography method based on frame sequence perceptual distance mapping. In this method, we introduce Learned Perceptual Image Patch Similarity (LPIPS) to quantify the similarity between consecutive video frames to obtain the sequential features of the video. Then we establish the relationship map between features and the hash sequence for information hiding. In addition, the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process. Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks. Compared with the existing methods, the robustness to Gaussian noise and speckle noise is improved by more than 40%, and the algorithm has better practicability and feasibility.

An efficient DCT-SBPM based video steganography in compressed domain

In this research work, an efficient robust video steganography in compressed domain is presented based on discrete cosine transform (DCT) encoded with H.264 codec. In this method, a compressed video (.mp4) of different resolutions, frame rates and sizes are used as a cover and RGB image is used as a secret message. Using appropriate stego key, the secret frames from the sequence of video frames are selected and the non-dynamic pixels of these frames are identified. The least significant bits (LSB) of the DCT component of these non-dynamic pixels are used as carrier object for hiding the R, G, and B components of secret message individually. An appropriate embedding key is designed to embed secret message in cover video such that the quality assessment parameters viz. Peak signal-to-noise ratio (PSNR) and mean square error (MSE) of imperceptibility can be optimized. Also, the successful extraction of secret message from the cover stego video is assessed by the robustness parameters viz. Bit error rate (BER) and similarity (Sim). The hiding capacity of proposed video steganography with respect to secret message and cover frame is measured by using hiding ratio (HR). The efficiency of proposed video steganography method is compared with the reported methodologies on the basis of the parameters of imperceptibility and robustness.

Spatiotemporal Generative Adversarial Network-Based Dynamic Texture Synthesis for Surveillance Video Coding

Dynamic texture refers to the content in video sequences that is characterized by spatial repetition and temporal variation, such as swaying foliage and flowing water. It is a great challenge to compress the dynamic textures efficiently in the current prediction/transform hybrid video coding framework. However, these textures have little information for machine vision, and human visual perception is less sensitive to the textures than to the structures. Thus, we propose a spatiotemporal generative adversarial network (GAN) based dynamic texture synthesis method for surveillance video coding. We detect and remove the dynamic texture content at encoder side, which is irrelevant to machine vision. We generate the dynamic texture content using the proposed GAN at decoder side, so that the reconstructed videos can be observed by human without deteriorating perceptual quality. Specifically, we design a GAN network to synthesize dynamic textures by exploiting the correlation between spatial and temporal neighbors; we present a surveillance video coding scheme with the dynamic texture detection/synthesis method; we build a high-quality dynamic texture dataset, and we collect a dynamic texture testing dataset that goes beyond the existing video coding test datasets by focusing on surveillance scenes. The proposed video coding scheme has been implemented on top of the High Efficiency Video Coding (HEVC) reference software. Experiments have been conducted to evaluate the quantitative and qualitative performance of the proposed coding scheme. Our method achieves 7.4% and 7.6% bit-rate savings in low-delay-B and low-delay-P settings, respectively, at similar visual quality levels in comparison with HEVC.

Chaos-based Video Steganography Method in Discrete Cosine Transform Domain

Due to the technological advancements in digital communication, the amount of multimedia content over the internet has increased manifold in past decade. This has renewed the internet of researchers in the area of privacy and secure communication. This paper presents a secure and robust video steganography method in discrete cosine transform (DCT) domain. In order to enhance the security of the proposed algorithm, the frame selection process is randomized and the secret data are pre-treated using Arnold’s cat map. The secret data are embedded in the middle band DCT coefficient using two pseudo random sequences. These sequences are generated using a chaotic map. We analyze the proposed algorithm in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), multi-scale structural similarity index (MSSIM) and video quality metric (VQM). The evaluation has been done on 107 video sequences. The experimental results demonstrate that the algorithm maintains acceptable video quality. The robustness of the proposed method is tested under Gaussian and salt and pepper noise attack using correlation between original and recovered images. The proposed algorithm is able to recover 90.60% data without error under salt and pepper noise ([Formula: see text]) attack and 87.23% data correctly under Gaussian noise attack with mean [Formula: see text] and variance [Formula: see text].

Efficient and secure data hiding in video sequence with three layer security: an approach using chaos

The fast development of communication and technology has created new challenges to transfer data securely. The techniques widely used to secure the data are cryptography and steganography. This paper presents a video steganography method to secure the information to be transmitted. Information transmitted can be an image, audio, text or video. This article presents a new technique that embeds data in the spatial domain of the cover video frame. The method employs chaotic maps to generate Random Positions (RP) to hide the information bits, random numbers for selecting the frames at which the information to be hidden and confusion order to encrypt the cover frame. The video frame is first selected based on Frame Selection (FS) is encrypted by applying Confusion Order (CO) and then embedding is carried out on the random positions generated. After embedding, the decrypted cover frame is replaced in a video sequence for transmission. This method provides three-level security in extracting the hidden secret information and also 25% of embedding capacity. Experimental outcomes (PSNR and payload) confirm that the method is competent.

A Novel Video Steganography-Based Botnet Communication Model in Telegram SNS Messenger

In botnets, a bot master regularly sends command and control messages (C & C messages) to bots for various purposes, such as ordering its commands to bots and collecting critical data from bots. Although such C & C messages can be encrypted by cryptographic methods to hide them, existing botnet detection mechanisms could detect the existence of botnets by capturing suspicious network traffics between the bot master (or the C & C server) and numerous bots. Recently, steganography-based botnets (stego-botnets) have emerged to make C & C communication traffics look normal to botnet detection systems. In stego-botnets, every C & C message is embedded in a multimedia file, such as an image file by using steganography techniques and shared in Social Network Service (SNS) websites (such as Facebook) or online messengers (such as WeChat or KakaoTalk). Consequently, traditional botnet detection systems without steganography detection methods cannot detect them. Meanwhile, according to our survey, we observed that existing studies on the steganography botnet are limited to use only image steganography techniques, although the video steganography method has some obvious advantages over the image steganography method. By this motivation, in this paper, we study a video steganography-based botnet in Social Network Service (SNS) platforms. We first propose a video steganography botnet model based on SNS messengers. In addition, we design a new payload approach-based video steganography method (DECM: Divide-Embed-Component Method) that can embed much more secret data than existing tools by using two open tools VirtualDub and Stegano. We show that our proposed model can be implemented in the Telegram SNS messenger and conduct extensive experiments by comparing our proposed model with DECM with an existing image steganography-based botnet in terms of C & C communication efficiency and undetectability.

A Video Steganography Method Based on Transform Block Decision for H.265/HEVC

High definition video application has drawn a lot of interest both from academy and industry. The relevant latest video coding technology, H.265/HEVC has been a promising area for video steganography. In this paper, we present a novel and efficient video steganography method based on transform block decision for H.265. In order to improve the visual quality of carrier video, we analyze the embedding error of data hiding with modifying partitioning parameters of CB, PB and TB, and modify the transform block decision to embed secret message and update corresponding residuals synchronously. In order to limit embedding error, we utilize an efficient embedding mapping rule which can embed N (N>1) bits message and at most modify one bit transform partitioning flag. Our experimental results show that the proposed method can achieve better visual quality, larger embedding capacity and less bit-rate increase than state-of-the-art researches.

Online Learning-Based Multi-Stage Complexity Control for Live Video Coding.

High Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this article, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.

A Bit Allocation Method Based on Inter-View Dependency and Spatio-Temporal Correlation for Multi-View Texture Video Coding

Due to the limited storage capacity and network bandwidth, an efficient rate control (RC) algorithm becomes more and more critical for multi-view video coding (MVC). Based on inter-view dependency and spatio-temporal correlation, a novel bit allocation method for multi-view texture video coding is proposed in this article. Firstly, considering that the distortion in the base view (BV) is directly transmitted to the dependent view (DV) by inter-view skip mode, a joint multi-view RD model is built based on the inter-view dependency. Based on the proposed joint multi-view RD model, a precise power model is derived to represent the target bitrates relationship between the BV and the DV. Secondly, since the P frame in the DV (P-DV) is mainly predicted from the corresponding I frame in the BV (I-BV) by disparity compensated prediction (DCP), the constant proportional relationship between the ratio of the average bitrates of the P-DV to the corresponding I-BV and the ratio of the total bitrates of the DV to the BV is discovered. Based on this discovery, a novel linear model is developed to assign the target bitrates of the P-DV. Finally, considering the spatio-temporal correlation, a new parameter prediction method is proposed for the R-$\lambda $ model in coding tree unit (CTU) level. Extensive experimental results show that the proposed overall method outperforms other state-of-the-art algorithms in terms of RD performance.

Parallel Video Steganographic Method over Multi-core Processors

Steganography refers to the manner in which secret data is concealed under a non-secret data. This non-secret data is known to be a digital form of media that are used as a cover. There are extensive researches regarding the images of steganographic methods. Nevertheless, the cover files did not receive enough attention. This study aims at providing a more inclusive approach for video steganography method. The difference in video steganography lies in the fact that they allow both sender and receiver to exchange hidden data through the matching between the secret text and the video frame RGB channels values presented in pixels, and manageable through different sizes of video files. The traditional manner contains the secret data under the video itself. The proposed method is designed over multithreading technique (parallelized method) for performance enhancement purposes. The results show that this developed steganographic method is more secure compared to the traditional steganographic method. In addition, results show that parallelism has impacted the proposed method positively.

Design and simulation a video steganography system by using FFT­turbo code methods for copyrights application

Protecting information on various communication media is considered an essential requirement in the present information transmission technology. So, there is a continuous search around different modern techniques that may be used to protect the data from the attackers. Steganography is one of those techniques that can be used to maintain the copyright by employing it to cover the publisher logo image inside the video frames. Nowadays, most of the popular known of the Video-Steganography methods become a conventional technique to the attacker, so there is a requirement for a modern and smart strategy to protect the copyright of the digital video file. Where this proposed system goal to create a hybrid system that combines the properties of Cryptography and Steganography work to protect the copyright hidden data from different attack types with maintaining of characteristics of the original video (quality and resolution). In this article, a modern Video-Steganography method is presented by employing the benefits of TC (Turbo code) to encrypt the pixels of logo image and Least two Significant Bit Technique procedure to embed the encryption pixels inside the frames of the video file. The insertion is performed in the frequency domain by applying the Fast Fourier Transform (FFT)on the video frames. The examination of the suggested architecture is done by terms of Structural Similarity Index, MSE (mean squared error), and PSNR (peak signal-to-noise ratio) by comparing between an original and extracted logo as well as between original and Steganographic video (averaged overall digital frames in the video). The simulation results show that this method proved high security, robustness, capacity and produces a substantial performance enhancement over the present known ways with fewer distortions in the quality of the video

Just Noticeable Distortion-Based Perceptual Rate Control in HEVC

In this paper, we propose a just noticeable distortion (JND)-based perceptual rate control method for high efficiency video coding (HEVC). First, the JND factor of a coding unit has been mathematically shown to be an approximation of the average pixel-level JND weight, which means that it can also be used as a weight for bitrate allocation. Second, rate-distortion (R-D) modelling is conducted based on the JND factor. Finally, the proposed R-D model is integrated into an existing rate control framework to improve the coding efficiency, and the proposed algorithm is implemented in the newest video coding standard. As the experimental results reveal, compared with HEVC reference software, our algorithm achieves significantly improved coding performance, subjective coding quality and bitrate accuracy.

A New Video Steganography Scheme Based on Shi-Tomasi Corner Detector

Recent developments in the speed of the Internet and information technology have made the rapid exchange of multimedia information possible. However, these developments in technology lead to violations of information security and private information. Digital steganography provides the ability to protect private information that has become essential in the current Internet age. Among all digital media, digital video has become of interest to many researchers due to its high capacity for hiding sensitive data. Numerous video steganography methods have recently been proposed to prevent secret data from being stolen. Nevertheless, these methods have multiple issues related to visual imperceptibly, robustness, and embedding capacity. To tackle these issues, this paper proposes a new approach to video steganography based on the corner point principle and LSBs algorithm. The proposed method first uses Shi-Tomasi algorithm to detect regions of corner points within the cover video frames. Then, it uses 4-LSBs algorithm to hide confidential data inside the identified corner points. Besides, before the embedding process, the proposed method encrypts confidential data using Arnold's cat map method to boost the security level. Experimental results revealed that the proposed method is highly secure and highly invisible, in addition to its satisfactory robustness against Salt & Pepper noise, Speckle noise, and Gaussian noise attacks, which has an average Structural Similarity Index (SSIM) of more than 0.81. Moreover, the results showed that the proposed method outperforms state-of-the-art methods in terms of visual imperceptibility, which offers excellent peak signal-to-noise ratio (PSNR) of average 60.7 dB, maintaining excellent embedding capacity.

A NOVEL METHOD FOR HDR VIDEO ENCODING, COMPRESSION AND QUALITY EVALUATION

The high dynamic range imaging/video has become more prominent in the consumer electronic realm as the it allows the human to view a picture e or a video at a minimum eye adaption. The high dynamic ranging is capable enough to capture a full range light from the scene unlike the traditional methods that captures only 8-f-stops. Though the high dynamic range video are produced using more standard methodologies, it still needs continuous developments in the distribution phase. so the paper discourses the shortcomings in the present method of high dynamic range video coding and puts forth the novel method of two layered backward compatible video encoding methods accompanied by the Chroma subsampling and the luminance masking to improve the color accuracy and remove the nonvisible portion respectively of the video content. The performance measurement of the novel method HDR video encoding compression (N-HVEC) in term of mean opinion source, bit rate savings and he HDR-VQM metric is compared with the HDR10 and HDR-HLG

Complexity reduction method for High Efficiency Video Coding encoding based on scene-change detection and image texture information

Recently, as demand for high-quality video and realistic media has increased, High Efficiency Video Coding has been standardized. However, High Efficiency Video Coding requires heavy cost in terms of computational complexity to achieve high coding efficiency, which causes problems in fast coding processing and real-time processing. In particular, High Efficiency Video Coding inter-coding has heavy computational complexity, and the High Efficiency Video Coding inter prediction uses reference pictures to improve coding efficiency. The reference pictures are typically signaled in two independent lists according to the display order, to be used for forward and backward prediction. If an event occurs in the input video, such as a scene change, the inter prediction performs unnecessary computations. Therefore, the reference picture list should be reconfigured to improve the inter prediction performance and reduce computational complexity. To address this problem, this article proposes a method to reduce computational complexity for fast High Efficiency Video Coding encoding using information such as scene changes obtained from the input video through preprocessing. Furthermore, reference picture lists are reconstructed by sorting the reference pictures by similarity to the current coded picture using Angular Second Moment, Contrast, Entropy, and Correlation, which are image texture parameters from the input video. Simulations are used to show that both the encoding time and coding efficiency could be improved simultaneously by applying the proposed algorithms.

A Relative Method of Video Coding using Different Wavelets with SPIHT

Lossy or lossless data compression is needed to Bring down the storage size and greater transmission rate. Wavelet transform is one of the ideal methods used for video compression. Different types of wavelet transforms are now available. In this paper, video is compressed by different wavelets and modified version of SPIHT. Totally seven types of wavelets are used here to compress the video, MSPIHT is used as encoding technique. They are (i) Video coding using duabechies wavelet and MSPIHT (VDM) (ii) Video coding using haar wavelet and MSPIHT (VHM) (iii) video coding using bi orthogonal wavelet and MSPIHT (VBM) (iv) video coding using symlet wavelet and MSPIHT (VSM) (v) video coding using coiflet wavelet and MSPIHT (VCM) (vi) video coding using demeyer wavelet and MSPIHT (VDMM) and (vii) video coding using Mexican hat wavelet and MSPIHT (VMHM). Then we calculate the PSNR and compression ratio for knowing the performance of the system.

A robust steganography method for HEVC based on secret sharing

This paper presents a robust steganography method for HEVC based on secret sharing. To prevent intra-frame distortion drift, three Classes of intra-frame prediction are defined. To improve the robustness of the secret message, the embedded message has been encoded into n sub-secrets by using (t, n)-threshold secret sharing. Then the encoded message is embedded into the multi-coefficients of the selected 4 × 4 luminance DST blocks which meet the Classes mentioned above. When the test video faced with different loss-rates, the survival rate of the proposed steganography method has increased by around 24.27%, 27.38%, 61.91% and 32.26% compared with Swati, Hayat, and Shahid (2014), respectively; the survival rate of the proposed steganography method has increased by around 28.74%, 34.60%, 56.05% and 41.40% compared with Liu, Liu, and Zhao (2017), respectively; when the loss-rate is less than 15%, the proposed steganography method can get 100% survival rate. And the experiments prove that the proposed method can achieve high performance than previously video steganography methods based on HEVC, especially for visual quality and robustness performance.

Digital video watermarking method based on heteroassociative image compression and its implementation by artificial neural netwoks

In this paper, we consider video steganography method based on using heteroassociative compressive transformation to embed user information into fragments of video frames. The results of testing the method in relation to visual distortion evaluation and accuracy of hidden data extraction using a number of known video codecs for compressing watermarked videos are presented.

Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation

The operations of digitization, transmission and storage of medical data, particularly images, require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. The 2nd-generation wavelets seeks to be positioned and confronted by the image and video coding methods currently used. It is in this context that we suggest a method combining bandelets and the SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage of capturing the geometrical complexity of the image structure. The second reason is the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements indicate that in some cases (for low bit rates) the performance of the proposed coding competes with the well-established ones (H.264 or MPEG4 AVC and H.265 or MPEG4 HEVC) and opens up new application prospects in the field of medical imaging.

Context-Based Ternary Tree Decision Method in Versatile Video Coding for Fast Intra Coding

This paper presents a fast encoding method for versatile video coding (VVC) using an early determination scheme that skips redundant multi-type tree (MTT) pruning. MTT consists of binary and ternary trees (TTs) with traditional quadtrees and has recently attracted considerable VVC research interest due to coding efficiency beyond HEVC. However, the additional trees, particularly TTs, significantly increased VVC complexity, which has rarely been studied previously. Therefore, we identified TT characteristics in the VVC encoding context, and hence propose a simple early decision technique that can effectively reduce TT complexity. Experiments over various test sequences show that the proposed method substantially decreased the total encoding time for a VVC test model 4.0 (VTM 4.0) with only slight coding loss under the all-intra configuration. In particular, the proposed method reduced TT encoding time by up to 66% compared with VTM 4.0.