Motion-compensated Frame Interpolation Methods Research Articles

Identification of Interpolated Frames by Motion-Compensated Frame-Interpolation via Measuring Irregularity of Optical Flow

Motion-compensated frame-interpolation (MCFI), synthesize intermediate frames between input frames guided by estimated motion, can be employed to falsify high bit-rate videos or high frame-rate videos with different frame-rates. Although existing MCFI identification methods have obtained satisfactory results, they are seriously degraded by stronger compression. Therefore, to conquer this issue, a blind forensics method is proposed to identify the adopted MCFI methods by considering the irregularities of optical flow produced by various MCFIs. In this paper, a set of compact features are constructed from the motion-aligned frame difference-weighted histogram of local binary pattern on the basis of optical flow (MAFD-WHLBP). Experimental results show that the proposed approach outperforms existing MCFI detectors under stronger compression.

Correlation Based Low Complex Video Frame Interpolation

Frame Interpolation is one of the main stages in video processing. Video coding standards skip some in-between frames for efficient compression and coding. At decoder the common approach to reconstruct the skipped frame using Motion Compensated Frame Interpolation (MCFI)methods. In MCFI, computational complexity is very high as calculation of Block Matching Algorithm, Motion Vectors (MV), Motion Estimation(ME) and Prediction logic of objects in motion between the frames, increases the complexity in MCFI method. A more feasible approach with minimum computational complexity using block level correlation is proposed in this paper. Errored MV at the decoder results in holes, occlusions, blurring and edge deformations in the interpolated frame. This proposal minimizes afore mentioned effects along with minimum complexity. The results are simulated in terms of peak-signal-to-noise-ratio (PSNR) and structural similarity index (SSIM).

Triple-Frame-Based Bi-Directional Motion Estimation for Motion-Compensated Frame Interpolation

In this paper, new motion estimation (ME) and motion vector refinement (MVR) methods for motion-compensated frame interpolation (MCFI) are proposed. To estimate motion vectors (MVs) with great reliability, triple-frame-based bi-directional ME (TFBME), which employs three sequential frames, is developed. In TFBME, the middle frame works as a reference to estimate MVs between two end frames, which leads to performance improvement especially when objects move linearly through three sequential frames. In addition, a modified bi-directional ME method, which employs only two successive frames, is combined with TFBME to handle cases in which objects move non-linearly through three sequential frames. In our proposed MVR, artifacts on interpolated frames are first detected by a convolutional neural network and then corrected by weighted vector median filtering. Experimental results show that the proposed MCFI method outperforms conventional methods in both subjective and objective evaluations.

Noise-level estimation based detection of motion-compensated frame interpolation in video sequences

Motion-Compensated Frame Interpolation (MCFI) is commonly used to produce the fake high-frame-rate videos, and it can be regarded as a video forgery operation from a broad sense. In this paper, we use the noise-level estimation to expose MCFI operator, and exploit the periodicity of noise-level varying to propose an effective automatic detection method. To guarantee the high detection accuracy, the high-pass filtering and the spike enhancement are both employed to extract the peak outliers in the Fourier domain. Depending on these outliers, we design the criterion of credibility value to make a final decision. The extensive experiments evaluated on hundreds of video sequences with different spatial resolutions and two parameter configurations of H.264/AVC have shown that the validity of the proposed method, which has the better detection accuracy for the MCFI method and the frame repetition.

Robust Motion Compensated Frame Interpolation Using Weight-Overlapped Block Motion Compensation with Variable Block Sizes to Reduce LCD Motion Blurs

Liquid crystal displays (LCDs) have slow responses, so motion blurs are often perceived in fast moving scenes. To reduce this motion blur, we propose a novel method of robust motion compensated frame interpolation (MCFI) based on bidirectional motion estimation (BME) and weight-overlapped block motion compensation (WOBMC) with variable block sizes. In most MCFI methods, a static block size is used, so some block artefacts and motion blurs are observed. However, the proposed method adjusts motion block sizes and search ranges by comparing matching scores, so the precise motion vectors can be estimated in accordance with motions. In the MCFI, overlapping ranges for WOBMC are also determined by adjusted block sizes, so the accurate MCFI can be performed. In the experimental results, the proposed method strongly reduced motion blurs arisen from large motions, and yielded interpolated images with high visual performance and peak signal-to-noise ratio (PSNR).

An Efficient Motion-Compensated Frame Interpolation Method Using Temporal Information for High-Resolution Videos

High-resolution (HR) videos have many different properties from low-resolution videos in terms of motion vectors (MVs) and occlusions. Therefore, it is necessary to develop an optimized motion-compensated frame interpolation (MCFI) method for HR videos. This paper proposes an MCFI method for HR videos that uses a hierarchical motion estimation composed of a modified 3-D recursive search and motion refinement using temporal MV smoothness. In addition, a robust frame interpolation method is proposed that assigns accurate MVs to the intermediate frame. The experimental results confirm that the proposed method outperforms previous MCFI methods in terms of the quality of the intermediate frames.

A Region-Based Motion-Compensated Frame Interpolation Method Using a Variance-Distortion Curve

This brief proposes a new motion-compensated frame interpolation (MCFI) method, and it generates new intermediate frames from existing original frames. The proposed method partitions the original frames into arbitrary-shaped regions and estimates motions for the regions. The region shapes were initially determined by pixel intensity and iteratively refined on the basis of a variance-distortion curve. The experimental results show that the proposed method more accurately interpolates frames than previous MCFI methods.

Iterative True Motion Estimation for Motion-Compensated Frame Interpolation

This paper proposes a new motion-compensated frame interpolation (MCFI) method. The performance of MCFI is highly dependent on the accuracy of the estimated motion vectors (MVs). The proposed iterative motion estimation (ME) method estimates reliable MVs using linearity checking and restricted search areas. An adaptive interpolation method is also proposed to obtain MVs for blocks that do not have reliable MVs in the iterative ME process. The experimental results show that the proposed method outperforms the conventional MCFI methods in terms of the quality of the interpolated frames. Furthermore, the MVs from the proposed method are more accurate than those from the conventional methods.

A Symmetric Motion Estimation Method for Motion-Compensated Frame Interpolation

This paper proposes a new motion-compensated frame interpolation (MCFI) method. The proposed method utilizes a symmetric motion estimation (SME) method, which is a new pixel-wise motion estimation method for intermediate frame interpolation. By using an adaptive search range for the motion estimation, the proposed method can obtain a more reliable motion vector for each pixel than previous MCFI methods that use a conventional block matching algorithm (BMA). In addition, we propose a combined method of the SME and BMA to reduce the computation time of the pixel-wise motion estimation method. The experimental results show that the proposed method outperforms other MCFI methods in terms of generating objectively and subjectively better interpolated frames.

Motion-Compensated Frame Interpolation Using a Parabolic Motion Model and Adaptive Motion Vector Selection

We propose a motion-compensated frame interpolation method in which an accurate backward/forward motion vector pair (MVP) is estimated based on a parabolic motion model. A reliability measure for an MVP is also proposed to select the most reliable MVP for each interpolated block. The possibility of deformation of bidirectional corresponding blocks is estimated from the selected MVP. Then, each interpolated block is produced by combining corresponding blocks with the weights based on the possibility of deformation. Experimental results show that the proposed method improves PSNR performance by up to 2.8 dB as compared to conventional methods and achieves higher visual quality without annoying blockiness artifacts.

Motion compensated frame interpolation based on H.264 decoder

A low complexity motion compensated frame interpolation method using compressed-domain information based on an H.264 decoder is presented. In the proposed method, the motion vectors are estimated using the constant acceleration motion model, and the interpolation algorithm is applied based on the macroblock coded types. Results show that the proposed method provides high quality interpolation frames.