Group Of Pictures Size Research Articles

Detection of HEVC double compression with non-aligned GOP structures via inter-frame quality degradation analysis

Detection of double compression is of great significance to reveal video compression history. However, efficient detection algorithm for High Efficiency Video Coding (HEVC) double compression with non-aligned Group of Pictures (GOP) structures is rarely reported. To address this issue, a novel algorithm based on inter-frame quality degradation process analysis is proposed in this paper. Firstly, a brief introduction to HEVC standard and double compression with non-aligned GOP structures is provided. Next, theoretical analysis of inter-frame quality degradation process is given. Then, from the perspective of filtering decision, the inter-frame In-Loop Filtering Decision Mode (ILFDM) feature is adopted for double compression detection. Finally, the detection framework is given, which includes feature extraction, feature combination and periodic analysis. Experiments are conducted under different coding scenarios, by comparing with existing algorithms, results demonstrate that the proposed algorithm has better performance not only in terms of double compression detection, but also in original GOP size estimation. Furthermore, the proposed scheme is proved to be satisfactory in relocated I-frame detection.

Detection of Double Compressed HEVC Videos Using GOP-Based PU Type Statistics

Double compression detection is of great significance to recover the compression history of suspicious videos and help analyzers locate tampering points in forged videos. However, the lack of efficient methods still haunts researchers when it comes to double HEVC compression detection with a shifted group of pictures (GOP) structures. To tackle this problem, we first analyze the properties of re-encoded frames in double compressed HEVC videos. It is found that the abnormal statistics of prediction unit (PU) types in relocated I-frames can be used as the clue to expose double compression. Relocated I-frames are re-encoded P-frames, which were I-frames in the first compression. Different from the existing works, we designed the GOP-based features instead of frame-based features, enabling more efficient utilization of temporal variation patterns of PU-type statistics within each GOP unit. In the proposed method, the ratio of I-PUs and S-Pus is first computed to construct PU sequences for each GOP unit. The three-tap median filter is applied to PU sequences in order to mitigate the influence of various video contents. Several first-order statistics, including average, variation, and maximum/minimum, are selected to generate a 6-D detection feature. Then, multi-layer perceptron (MLP) is used to classify GOP-based features. Finally, the score fusion strategy is applied to obtain the detection result of the input video. Several publicly available YUV sequences are used to construct the double compression dataset with various coding parameters. In experiments, the proposed method outperforms several state-of-the-art methods under different settings of bitrates and GOP structures. The proposed method has more robust detection capability even when tampered videos are generated by combining two video clips with different GOP sizes.

A Hybrid Tactic Model Intended for Video Compression Using Global Affine Motion and Local Free-Form Transformation Parameters

Video compression marks its necessity when a huge sized video needs to be transmitted. The process starts with the identification of GoP (group of pictures), which depends on I- (intra), B- (bidirectional) and P- (predicted) frames determination. GoP is fixed, where consecutive frames are placed in an orderly manner based on the GoP size. Conventionally, B-frames lead to buffering of memory within the past and future frames consuming more computational time. Such issues are handled by an adaptive framework for determining frames based on matching criteria rather than fixed GoP. NSEW (North–South–East–West) affine translation (NAT) is proposed for replacing B with either I- or P-frame. The proposed framework involves video compression using affine motion-based free-form transformation and video decompression using warping methodologies for the purpose of compressing and decompressing the video sequence, based on the resulted I- and P-frames. B-spline transformation was also initiated at local level along with global affine transformation to improve the subjective quality of the decompressed video sequence. The methodology was investigated for the file size, computational time, peak-signal-to-noise ratio (PSNR) and Structural Similarity index (SSIM), which proved the superiority of the proposed technique. Further, the methodology was also investigated with optimizing the affine motion parameters (AMP) using nonlinear least squares, Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS which yet again proved to be far more superior to conventional techniques with an average PSNR of 38.98 dB with LBFGS. To further improve the subjective quality, affine B-spline-based motion estimation using LBFGS was implemented and observed the average PSNR gain to be 42.03 dB.

Codec and GOP Identification in Double Compressed Videos.

Video content is routinely acquired and distributed in a digital compressed format. In many cases, the same video content is encoded multiple times. This is the typical scenario that arises when a video, originally encoded directly by the acquisition device, is then re-encoded, either after an editing operation, or when uploaded to a sharing website. The analysis of the bitstream reveals details of the last compression step (i.e., the codec adopted and the corresponding encoding parameters), while masking the previous compression history. Therefore, in this paper, we consider a processing chain of two coding steps, and we propose a method that exploits coding-based footprints to identify both the codec and the size of the group of pictures (GOPs) used in the first coding step. This sort of analysis is useful in video forensics, when the analyst is interested in determining the characteristics of the originating source device, and in video quality assessment, since quality is determined by the whole compression history. The proposed method relies on the fact that lossy coding is an (almost) idempotent operation. That is, re-encoding a video sequence with the same codec and coding parameters produces a sequence that is similar to the former. As a consequence, if the second codec in the chain does not significantly alter the sequence, it is possible to analyze this sort of similarity to identify the first codec and the adopted GOP size. The method was extensively validated on a very large data set of video sequences generated by encoding content with a diversity of codecs (MPEG-2, MPEG-4, H.264/AVC, and DIRAC) and different encoding parameters. In addition, a proof of concept showing that the proposed method can also be used on videos downloaded from YouTube is reported.

Fusion of Global and Local Motion Estimation Using Foreground Objects for Distributed Video Coding

The side information (SI) in Distributed Video Coding (DVC) is estimated using the available decoded frames and exploited for the decoding and reconstruction of other frames. The quality of the SI has a strong impact on the performance of DVC. Here, we propose a new approach that combines both global and local SI to improve coding performance. Since the background pixels in a frame are assigned to global estimation and the foreground objects to local estimation, one needs to estimate foreground objects in the SI using the backward and forward foreground objects, the background pixels are directly taken from the global SI. Specifically, elastic curves and local motion compensation are used to generate the foreground objects masks in the SI. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.39 dB for a group of picture (GOP) size of 2, and up to 4.73 dB for larger GOP sizes, with respect to the reference DISCOVER codec.

Distributed video coding supporting hierarchical GOP structures with transmitted motion vectors

Abstract In this paper, we propose a new distributed video coding (DVC) method, with hierarchical group of picture (GOP) structure. Coding gain of DVC can be significantly improved by enlarging GOP size for slow-moving frames. The proposed DVC decoder estimates a side information (SI) frame and transmits motion vectors (MVs) of the SI to the proposed encoder. Using the received MVs from the decoder, the proposed encoder can generate a predicted SI (PSI), which is the same as the SI in the decoder, and estimate the quality of PSI with minimal computational complexity. The proposed method decides the best coding mode among key, Wyner-Ziv (WZ), and skip modes, by estimating rate-distortion costs. Based on the selected best coding mode, the best GOP size can be automatically determined. As the GOP size is adaptively decided depending on the SI quality, entropy and parity bits can be effectively consumed. Experimental results show that the proposed algorithm is around 0.80 dB better in Bjøntegaard delta (BD) bitrate than an existing conventional DVC system.

ADAPTIVE GOP STRUCTURE TO H.264/AVC BASED ON SCENE CHANGE

This paper proposes an adaptive GOP structure with the new logic of frame comparison in H.264/AVC to achieve better quality and reduce bit rate. Initially Group of Pictures (GOP) is set to a fixed size. Frames are compared within that GOP using correlation. According to the correlation, GOP is changed within that fixed size. So, there will be no GOP size greater than that fixed size. This method does not calculate any threshold. Hence the time needed to calculate global or local threshold is eliminated. It is integrated with conventional video codec H.264/AVC. This method is compared with H.264/AVC of fixed GOP structure of sizes 4, 8, 12, 16, 32 and GOP structure with the length of entire video. The proposed method achieved gain in bit rate from 0.49% to 69.75% and PSNR gain from 2.5% to 0.3%.

Content-based group-of-picture size control in distributed video coding

Controlling the group of picture (GOP) size in distributed video coding (DVC) is a difficult but important task since it has a direct impact on the coding performance. This paper presents a framework to adaptively control the size of GOPs in a Wyner–Ziv encoder by means of encoder-side decisions based on support vector machines (SVM) that uses simple features extracted from the original video content. To train the SVM, firstly this work proposes how to compute the sequence of GOP sizes with the best rate-distortion performance given the set of GOP sizes that can be used during the encoding process. Then, an algorithm based on the previously trained SVMs is presented to control the actual GOP size each time a new decision can be taken at the encoder. Results show that the proposed algorithm can achieve a rate distortion performance close to the ideal one. Moreover, comparisons with a reference adaptive GOP size selection algorithm in the literature shows gains up to 2dB PSNR in the best conditions.

Progressively refined wyner-ziv video coding for visual sensors

Wyner-Ziv video coding constitutes an alluring paradigm for visual sensor networks, offering efficient video compression with low complexity encoding characteristics. This work presents a novel hash-driven Wyner-Ziv video coding architecture for visual sensors, implementing the principles of successively refined Wyner-Ziv coding. To this end, so-called side-information refinement levels are constructed for a number of grouped frequency bands of the discrete cosine transform. The proposed codec creates side-information by means of an original overlapped block motion estimation and pixel-based multihypothesis prediction technique, specifically built around the pursued refinement strategy. The quality of the side-information generated at every refinement level is successively improved, leading to gradually enhanced Wyner-Ziv coding performance. Additionally, this work explores several temporal prediction structures, including a new hierarchical unidirectional prediction structure, providing both temporal scalability and low delay coding. Experimental results include a thorough evaluation of our novel Wyner-Ziv codec, assessing the impact of the proposed successive refinement scheme and the supported temporal prediction structures for a wide range of hash configurations and group of pictures sizes. The results report significant compression gains with respect to benchmark systems in Wyner-Ziv video coding (e.g., up to 42.03% over DISCOVER) as well as versus alternative state-of-the-art schemes refining the side-information.

Fusion of Global and Local Motion Estimation for Distributed Video Coding

The quality of side information plays a key role in distributed video coding. In this paper, we propose a new approach that consists of combining global and local motion compensation at the decoder side. The parameters of the global motion are estimated at the encoder using scale invariant feature transform features. Those estimated parameters are sent to the decoder in order to generate a globally motion compensated side information. Conversely, a locally motion compensated side information is generated at the decoder based on motion-compensated temporal interpolation of neighboring reference frames. Moreover, an improved fusion of global and local side information during the decoding process is achieved using the partially decoded Wyner-Ziv frame and decoded reference frames. The proposed technique improves significantly the quality of the side information, especially for sequences containing high global motion. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.9 dB for a Group of Pictures (GOP) size of 2, and up to 4.65 dB for larger GOP sizes, with respect to the reference DISCOVER codec.

Trellis-based optimization of layer extraction for rate adaptation in real-time scalable stereo video coding

The concept of quality layers (QLs) has been adopted in the scalable video coding standard to enable optimal rate adaptation of precoded video in the rate-distortion sense. QLs were previously extended to stereo and multiple-view scalable video for efficient transport of 3DTV over the Internet. However, it is not possible to use the QL method in applications that require real-time encoding since the priority determination process assumes the availability of the whole video sequence. In this work, a trellis-based online rate adaptation is proposed for real-time scalable stereo video coding, with a delay of 1 group of pictures (GoP). The delay can be controlled by selection of the GoP size according to the application, such as 16 frames for live broadcast or 8 or 4 frames for videoconferencing. In addition, the joint optimization of layer extraction for scalable multiview coded stereo video is also proposed. It is assumed that the encoder/extractor is aware of the available dynamic network bandwidth in order to perform rate-distortion optimized medium-grain fidelity scalability layer selection for each GoP. Experimental results show that the performance of the proposed online method is very close to that of QLs that would require the whole video sequence.

Refining Side Information for Improved Transform Domain Wyner-Ziv Video Coding

Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, which is a recent video coding paradigm based on the Slepian-Wolf and WZ theorems. Contrary to available prediction-based standard video codecs, WZ video coding exploits the source statistics at the decoder, allowing the development of simpler encoders. Until now, WZ video coding did not reach the compression efficiency performance of conventional video coding solutions, mainly due to the poor quality of the side information, which is an estimate of the original frame created at the decoder in the most popular WZ video codecs. In this context, this paper proposes a novel side information refinement (SIR) algorithm for a transform domain WZ video codec based on a learning approach where the side information is successively improved as the decoding proceeds. The results show significant and consistent performance improvements regarding state-of-the-art WZ and standard video codecs, especially under critical conditions such as high motion content and long group of pictures sizes.

Error Resilient Coding and Error Concealment in Scalable Video Coding

Scalable video coding (SVC), which is the scalable extension of the H.264/AVC standard, was developed by the Joint Video Team (JVT) of ISO/IEC MPEG (Moving Picture Experts Group) and ITU-T VCEG (Video Coding Experts Group). SVC is designed to provide adaptation capability for heterogeneous network structures and different receiving devices with the help of temporal, spatial, and quality scalabilities. It is challenging to achieve graceful quality degradation in an error-prone environment, since channel errors can drastically deteriorate the quality of the video. Error resilient coding and error concealment techniques have been introduced into SVC to reduce the quality degradation impact of transmission errors. Some of the techniques are inherited from or applicable also to H.264/AVC, while some of them take advantage of the SVC coding structure and coding tools. In this paper, the error resilient coding and error concealment tools in SVC are first reviewed. Then, several important tools such as loss-aware rate-distortion optimized macroblock mode decision algorithm and error concealment methods in SVC are discussed and experimental results are provided to show the benefits from them. The results demonstrate that PSNR gains can be achieved for the conventional inter prediction (IPPP) coding structure or the hierarchical bi-predictive (B) picture coding structure with large group of pictures size, for all the tested sequences and under various combinations of packet loss rates, compared with the basic joint scalable video model (JSVM) design applying no error resilient tools at the encoder and only picture copy error concealment method at the decoder.

An adaptive GOP structure selection for haar-like MCTF encoding based on mutual information

In conventional motion compensated temporal filtering based wavelet coding scheme, where the group of picture structure and low-pass frame position are fixed, variations in motion activities of video sequences are not considered. In this paper, we propose an adaptive group of picture structure selection scheme, which the group of picture size and low-pass frame position are selected based on mutual information. Furthermore, the temporal decomposition process is determined adaptively according to the selected group of picture structure. A large amount of experimental work is carried out to compare the compression performance of proposed method with the conventional motion compensated temporal filtering encoding scheme and adaptive group of picture structure in standard scalable video coding model. The proposed low-pass frame selection can improve the compression quality by about 0.3---0.5 dB comparing to the conventional scheme in video sequences with high motion activities. In the scenes with un-even variation of motion activities, e.g. frequent shot cuts, the proposed adaptive group of picture size can achieve a better compression capability than conventional scheme. When comparing to adaptive group of picture in standard scalable video coding model, the proposed group of picture structure scheme can lead to about 0.2~0.8 dB improvements in sequences with high motion activities or shot cut.

Adaptive group-of-pictures and scene change detection methods based on existing H.264 advanced video coding information

The H.264 advanced video coding (H.264/AVC) standard provides several advanced features such as improved coding efficiency and error robustness for video storage and transmission. In order to improve the coding performance of H.264/AVC, coding control parameters such as group-of-pictures (GOP) sizes should be adaptively adjusted according to different video content variations (VCVs), which can be extracted from temporal deviation between two consecutive frames. The authors present a simple VCV estimation to design adaptive GOP detection (AGD) and scene change detection (SCD) methods by using the obtained motion information, where the motion vectors and the sum of absolute transformed differences as VCV features are effectively used to design the AGD and SCD algorithms, respectively. In order to avoid unnecessary computation, the above VCV features are obtained only in the 4times4 inter-frame prediction mode. Simulation results show that the proposed AGD with SCD methods can increase the peak signal-to-noise ratio by 0.62 dB on average over the H.264/AVC operated with a fixed GOP size. Besides, the proposed SCD method can reach a scene change detection rate of 98%.

New Architecture for MPEG Video Streaming System With Backward Playback Support

MPEG digital video is becoming ubiquitous for video storage and communications. It is often desirable to perform various video cassette recording (VCR) functions such as backward playback in MPEG videos. However, the predictive processing techniques employed in MPEG severely complicate the backward-play operation. A straightforward implementation of backward playback is to transmit and decode the whole group-of-picture (GOP), store all the decoded frames in the decoder buffer, and play the decoded frames in reverse order. This approach requires a significant buffer in the decoder, which depends on the GOP size, to store the decoded frames. This approach could not be possible in a severely constrained memory requirement. Another alternative is to decode the GOP up to the current frame to be displayed, and then go back to decode the GOP again up to the next frame to be displayed. This approach does not need the huge buffer, but requires much higher bandwidth of the network and complexity of the decoder. In this paper, we propose a macroblock-based algorithm for an efficient implementation of the MPEG video streaming system to provide backward playback over a network with the minimal requirements on the network bandwidth and the decoder complexity. The proposed algorithm classifies macroblocks in the requested frame into backward macroblocks (BMBs) and forward/backward macroblocks (FBMBs). Two macroblock-based techniques are used to manipulate different types of macroblocks in the compressed domain and the server then sends the processed macroblocks to the client machine. For BMBs, a VLC-domain technique is adopted to reduce the number of macroblocks that need to be decoded by the decoder and the number of bits that need to be sent over the network in the backward-play operation. We then propose a newly mixed VLC/DCT-domain technique to handle FBMBs in order to further reduce the computational complexity of the decoder. With these compressed-domain techniques, the proposed architecture only manipulates macroblocks either in the VLC domain or the quantized DCT domain resulting in low server complexity. Experimental results show that, as compared to the conventional system, the new streaming system reduces the required network bandwidth and the decoder complexity significantly.

Rate-Distortion Analysis of Motion-Compensated Interpolation at the Decoder in Distributed Video Coding

This letter analyzes the coding efficiency of distributed video coding (DVC) schemes that perform motion-compensated interpolation at the decoder. The decoder has access only to the key frames, when generating the side information for intermediate frames. Therefore, the true motion field necessary for this operation is not directly available, and the motion vectors must be estimated at the decoder side, thus introducing displacement estimation errors. The accuracy of the motion-compensated interpolation at the decoder depends on several factors: 1, the overall motion complexity; 2, the temporal coherence of the motion field; and 3, the temporal distance between successive key frames. Adopting a state-space model and a Kalman filtering framework, we obtain an estimate of the displacement error variance. This is used to determine the rate-distortion function of the overall coding scheme, that takes into account both intra-coded key frames and DVC-coded frames. The proposed model shows that motion-compensated interpolation is unable to achieve the coding efficiency of conventional motion-compensated predictive coding. In addition, the model provides a good estimate of the group of pictures size that optimizes the coding efficiency. Experimental results on real video sequences validate the results of the proposed model.

A high quality VBR MPEG system for Internet and broadcasting applications

A high quality variable bit rate (VBR) MPEG-2 encoding scheme that offers optimum video quality and bandwidth allocation is presented. This scheme utilizes a real-time bit redistribution method, insertion of I frames at scene changes and large GOP (group of pictures) size to produce high picture quality. An encoders-bank with predetermined sets of nominal-long-term, minimum and maximum bit rates in combination with a sophisticated picture quality control process allows a bit rate selector to choose the optimum bit stream from a set of encoders, thus resulting in a high quality VBR video stream. The proposed scheme is very easy to implement and offers an efficient real-time VBR solution for broadcasting applications that involve multiplexed distribution of multiple video sources.