A new path based interpolation using object motion for frame rate up conversion

비디오 프레임 율 증가 변환은 최근에 가전 분야에서 비디오 정보원과 더불어 디스플레이 포맷의 폭발적인 증가에 힘입어 매우 많은 관심의 대상이 되었다. 대부분의 진보된 FRUC 알고리즘은 보간된 프레임들의 움직임 벡터장을 결정하는 효과적인 움직임 보간 기술을 사용하고 있다. 그러나 수신기의 후처리 분야와 같은 응용 분야에서는 움직임 보상 보간 프레임이 얼마나 잘 복원되었는지에 대한 정보를 필요로 한다. 이와 같은 목적을 달성하기 위해, 먼저, 본 논문에서는 움직임 보상 보간 프레임의 블록 단위로 신뢰도를 측정할 수 있는 비용 함수들을 도입한다. 그런 후에 이러한 함수들을 사용하여 움직임 보상 보간 프레임에서 얼마나 많은 잡음이 포함되어 있는지를 평가할 수 있는 두 가지의 왜곡 예측 모델을 제안한다. 모의실험을 통하여 제안된 왜곡 예측 방식은 움직임 보상 보간 프레임의 잡음을 효과적으로 예측할 수 있음을 보인다. Video FRUC (Frame Rate Up Conversion) is one of the main issues that have arisen in recent years with the explosive growth of video sources and display formats in consumer electronics. Most advanced FRUC algorithms adopt an efficient motion interpolation technique to determine the motion vector field of interpolated frames. But, in some application areas such as post processing in receiver side, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame was reconstructed. In order to achieve this aim, first, this paper introduces some cost functions to estimate the reliability of a block in the MCI frame. Then, by using these functions, this paper proposes two distortion estimation models for evaluating how much noise was produced in the MCI frame. Through computer simulations, it is shown that the proposed estimation methods perform effectively in estimating the noises of the MCI frame.

We present a novel and effective learning-based frame rate upconversion (FRUC) scheme, using linear mapping. The proposed learning-based FRUC scheme consists of: 1) a new hierarchical extended bilateral motion estimation (HEBME) method; 2) a light-weight motion deblur (LWMD) method; and 3) a synthesis-based motion-compensated frame interpolation (S-MCFI) method. First, the HEBME method considerably enhances the accuracy of the motion estimation (ME), which can lead to a significant improvement of the FRUC performance. The proposed HEBME method consists of two ME pyramids with a three-layered hierarchy, where the motion vectors (MVs) are searched in a coarse-to-fine manner via each pyramid. The found MVs are further refined in an enhanced resolution of four times by jointly combining the MVs from the two pyramids. The HEBME method employs a new elaborate matching criterion for precise ME which effectively combines a bilateral absolute difference, an edge variance, pixel variances, and an MV difference among two consecutive blocks and its neighboring blocks. Second, the LWMD method uses the MVs found by the HEBME method and removes the small motion blurs in original frames via transformations by linear mapping. Third, the S-MCFI method finally generates interpolated frames by applying linear mapping kernels for the deblurred original frames. In consequence, our FRUC scheme is capable of precisely generating interpolated frames based on the HEBME for accurate ME, the S-MCFI for elaborate frame interpolation, and the LWMD for contrast enhancement. The experimental results show that our FRUC significantly outperforms the state-of-the-art non-deep learning-based schemes with an average of 1.42 dB higher in the peak signal-to-noise-ratio and shows comparable performance with the state-of-the-art deep learning-based scheme.

DVC (Distributed Video Coding) and FRUC (Frame Rate Up Conversion) techniques need to have an efficient motion compensated frame interpolation algorithms. Conventional works of these applications have mainly focused on the performance improvement of overall system. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame matches the original frame. For this aim, this paper deals with the modeling methods for evaluating the block-based matching cost. First, several matching criteria, which have already been dealt with the motion compensated frame interpolation, are introduced and then combined to make estimate models for the size of MSE (Mean Square Error) noise of the MCI frame to original one. Through computer simulations, it is shown that the block-based matching criteria are evaluated and the proposed model can be effectively used for estimating the MSE noise.

In this work, we proposed an improved frame rate up-conversion (FRUC) algorithm based on dual motion estimation (DME), which combines the unidirectional motion estimation and bi-directional motion estimation. Considering that edge of the image has a significant impact on the interpolated frame and in order to heighten the accuracy of motion estimation at edge area, edge information is added to the motion estimation process. Besides, the overlapped block motion estimation (OBME) method is applied to further improve the accuracy of motion vectors. Then an median filter is used to detect abnormal vector and correct it to obtain the final motion vector field. In the phase of motion compensation, this algorithm introduces block boundary distortion match to improve the lacking of traditional overlapped block motion compensation method (OBMC). The experimental results verify that the FRUC algorithm proposed in this paper outstands other algorithms in subjective and objective quality.

Motion-compensated frame rate up-conversion (MC-FRUC) increases the frame rate of a video by interpolating a new frame from temporally consecutive frames. MC-FRUC consists of two key operations: motion estimation and motion compensated frame interpolation. Motion compensation along unidirectional motion trajectories directly causes overlaps and holes issues. To solve these issues, this paper presents a new algorithm for bidirectional motion compensated frame interpolation. Firstly, the proposed method generates a bidirectional motion vector field from two unidirectional motion vector fields (forward and backward) obtained from the motion estimation. It is done by projecting the forward and backward motion vectors into the interpolated frame. A comprehensive metric as an extension of the distance between a projected block and an interpolated block is proposed to compute weighted coefficients in case the interpolated block has multiple projected ones. Holes are filled based on vector median filter of non-hole available neighbor blocks. The proposed method outperforms existing MC-FRUC methods and removes block artifacts significantly.

Liquid crystal displays (LCDs), which serve as receivers of high visual quality video, suffer from motion blur issues. One of the methods in terminating motion blur is motion compensated frame rate upconversion, which is widely adopted in LCDs. In the previous work, i.e., particle-based frame rate upconversion, the computation complexity is high while repeated operations and some improper cost evaluation setup are observed. Therefore, in this paper, hybrid frame rate upconversion is proposed with two features. First, the cost evaluation for particle-based motion trajectory calibration is modified based on the possible noise sources and video resolution variations. Second, repeated operations in particle-based motion trajectory calibration are observed. Therefore, original particle-based motion trajectory calibration is replaced by initial motion vector assignment and subsequent motion vector mapping to achieve computation complexity reduction, while the effective search range is relatively expanded. According to the experiment results, the visual quality is enhanced by 1.87 dB on average, compared with state-of-the-art unidirectional-based frame rate upconversion approaches. On the other hand, the computation complexity of the proposed design is reduced by 25-90% based on target video resolution, concluding a high visual quality and low computation complexity frame rate upconversion design.

A new bilateral frame rate up-conversion (FRUC) algorithm based on the bi-directional comparison of the texture complexity in the reference frame is proposed. In the proposed algorithm, the accuracy of the motion estimation (ME) process is determined by the comparison of the prediction size in the reference region of the bi-directional ME process. Base on this comparison, the adaptive refinement process is adjusted. In the experimental results, the proposed algorithm outperforms the conventional bilateral FRUC algorithm by 0.27 dB.

In this paper, we propose a new motion compensated frame interpolation (MCFI) method for occlusion and motion ambiguity regions of frame rate up-conversion (FRUC) assisted by depth information in color-plus-depth video. First, we estimate motion vectors in a similar way to conventional methods and refine them using depth information. In the intermediate frame, we try to solve challenging FRUC problems such as occlusion, boundary, and motion ambiguity using depth information. Finally, interpolation is performed to generate intermediate frames. Experiments were performed to compare the proposed method and conventional color and color-plus-depth FRUC methods. The experiment result shows that the proposed method using depth information can improve the FRUC performance.

This paper presents a new framework for motion compensated frame rate up conversion (FRUC) based on variational image fusion. The proposed algorithm consists of two steps: 1) generation of multiple intermediate interpolated frames and 2) fusion of those intermediate frames. In the first step, we determine four different sets of the motion vector field using four neighboring frames. We then generate intermediate interpolated frames corresponding to the determined four sets of the motion vector field, respectively. Multiple sets of the motion vector field are used to solve the occlusion problem in motion estimation. In the second step, the four intermediate interpolated frames are fused into a single frame via a variational image fusion process. For effective fusion, we determine fusion weights for each intermediate interpolated frame by minimizing the energy, which consists of a weighted-L1-norm based data energy and gradient-driven smoothness energy. Experimental results demonstrate that the proposed algorithm improves the performance of FRUC compared with the existing algorithms.

본 논문은 EBME(Extended Bilateral Motion Estimation) 알고리듬에서 동일한 움직임 벡터들의 방향과 크기를 고려한 알고리듬을 제안하였다. EBME는 BME(Bilateral Motion Estimation)보다 높은 연산량을 요구하기 때문에 프레임 내의 x, y방향 각각의 평균 움직임 벡터크기를 이용하여 동적프레임과 정적프레임을 판단하고, EBME 수행여부를 판단하여 연산량을 줄인다. 또한 동일한 움직임 벡터들의 방향과 크기를 비교하여 MVS(Motion Vector Smoothing)단계 수행여부를 판단함으로써 연산량을 줄인다. 제안하는 알고리듬을 적용한 실험 결과 기존의 EBME 알고리듬에 비해 수행시간은 단축되고 PSNR(Peak Signal to Noise Ratio)은 향상 되었다. In this paper, frame rate up conversion (FRUC) algorithm considering the direction and magnitude of identical motion vectors is proposed. extended bilateral motion estimation (EBME) has higher complexity than bilateral motion estimation (BME). By using average magnitude of motion vector with x and y direction respectively, dynamic frame and static frame are decided. We reduce complexity to decide EBME. also, After we compare the direction and magnitude of identical motion vectors, We reduce complexity to decide motion vector smoothing(MVS). Experimental results show that this proposed algorithm has fast computation and better peak singnal to noise ratio(PSNR) results compared with EBME.

The rise of useful multimedia applications has sparked a rush to perfect video compression methods. Over the last decade, there has been a lot of interest in using motion-compensated frame interpolation (MCFI) methods to boost video frame rate during playback in both the academic and consumer electronics sectors. Interpolation refers to the process of defining the values of a function at places that are not represented by any of the examples. It accomplishes this process by fitting a constant function across the discrete input samples. We based our methodology on the fact that color cameras function similarly to the human eye. We have been used to black and white photographs, yet the real world is full of color. Grayscale images were the sole option until the advent of color monitors and cameras. Energy assessment calls for photo capture using a camera. Light's electromagnetic waves provide the observable energy in this scheme. For motion-compensated video vector frame interpolation or frame rate up-conversion and also Phase frame interpolation Method, the authors present a novel approach with low vector dispensation method in the conclusion. Using optical flow or other conventional methods, it is necessary to have precise pixel correspondences between pictures in order to compute interpolated frames in a video series. We provide a practical replacement by capitalizing on advances in phase-based approaches, which use the phase shift of individual pixels to express motion. In challenging interpolation conditions, such as major appearance changes, flow-based algorithms frequently create severe visual distortions, yet our solution fails gently. Our approach is particularly useful for retiming and interpolating frames in high-resolution video shot at a high frame rate. Estimating motion for the purpose of improving the quality of the decompressed video requires more research on the nature of the compression problem. a novel way to estimating motion that is analogous to full-search 3ss,4ss, block-matching, and parametric techniques. The primary focus will be research into both error-free compression and the development of novel motion estimation algorithms. The results of the research indicate that the suggested system has the potential to increase visual superiority and is also robust, especially when applied to video sequences with quick movements and challenging portions. By combining phase-based and conventional Lagrangian, which is utilized to get better quality results and deal with variations in light more gracefully.

The rise of useful multimedia applications has sparked a rush to perfect video compression methods. Over the last decade, there has been a lot of interest in using motion-compensated frame interpolation (MCFI) methods to boost video frame rate during playback in both the academic and consumer electronics sectors. Interpolation refers to the process of defining the values of a function at places that are not represented by any of the examples. It accomplishes this process by fitting a constant function across the discrete input samples. We based our methodology on the fact that color cameras function similarly to the human eye. We have been used to black and white photographs, yet the real world is full of color. Grayscale images were the sole option until the advent of color monitors and cameras. Energy assessment calls for photo capture using a camera. Light's electromagnetic waves provide the observable energy in this scheme. For motion-compensated video vector frame interpolation or frame rate up-conversion and Phase frame interpolation Method, the authors present a novel approach with low vector dispensation method in the conclusion. Using optical flow or other conventional methods, it is necessary to have precise pixel correspondences between pictures to compute interpolated frames in a video series. We provide a practical replacement by capitalizing on advances in phase-based approaches, which use the phase shift of individual pixels to express motion. In challenging interpolation conditions, such as major appearance changes, flow-based algorithms frequently create severe visual distortions, yet our solution fails gently. Our approach is particularly useful for retiming and interpolating frames in high-resolution video shot at a high frame rate. Estimating motion for the purpose of improving the quality of the decompressed video requires more research on the nature of the compression problem. a novel way to estimating motion that is analogous to full-search 3ss,4ss, block-matching, and parametric techniques. The primary focus will be research into both error-free compression and the development of novel motion estimation algorithms. The results of the research indicate that the suggested system has the potential to increase visual superiority and is also robust, especially when applied to video sequences with quick movements and challenging portions. By combining phase based and conventional Lagrangian, which is utilized to get better quality results and deal with variations in light more gracefully.

The rise of useful multimedia applications has sparked a rush to perfect video compression methods. Over the last decade, there has been a lot of interest in using motion-compensated frame interpolation (MCFI) methods to boost video frame rate during playback in both the academic and consumer electronics sectors. Interpolation refers to the process of defining the values of a function at places that are not represented by any of the examples. It accomplishes this process by fitting a constant function across the discrete input samples. We based our methodology on the fact that color cameras function similarly to the human eye. We have been used to black and white photographs, yet the real world is full of color. Grayscale images were the sole option until the advent of color monitors and cameras. Energy assessment calls for photo capture using a camera. Light's electromagnetic waves provide the observable energy in this scheme. For motion-compensated video vector frame interpolation or frame rate up-conversion and Phase frame interpolation Method, the authors present a novel approach with low vector dispensation method in the conclusion. Using optical flow or other conventional methods, it is necessary to have precise pixel correspondences between pictures in order to compute interpolated frames in a video series. We provide a practical replacement by capitalizing on advances in phase-based approaches, which use the phase shift of individual pixels to express motion. In challenging interpolation conditions, such as major appearance changes, flow-based algorithms frequently create severe visual distortions, yet our solution fails gently. Our approach is particularly useful for retiming and interpolating frames in high-resolution video shot at a high frame rate. Estimating motion for the purpose of improving the quality of the decompressed video requires more research on the nature of the compression problem. a novel way to estimating motion that is analogous to full-search 3ss,4ss, block-matching, and parametric techniques. The primary focus will be research into both error-free compression and the development of novel motion estimation algorithms. The results of the research indicate that the suggested system has the potential to increase visual superiority and is also robust, especially when applied to video sequences with quick movements and challenging portions. By combining phase based and conventional Lagrangian, which is utilized to get better quality results and deal with variations in light more gracefully.

Frame rate up-conversion is a postprocessing tool to convert the frame rate from a lower number to a higher one. It is a useful technique for a lot of practical applications, such as display format conversion, low bit rate video coding and slow motion playback. Unlike traditional approaches, such as frame repetition or linear frame interpolation, motion-compensated frame interpolation (MCFI) technique is more efficient since it takes block motion into account. In this paper, by considering the deficiencies of previous works, new criteria and coding schemes for improving motion derivation and interpolation processes are proposed. Next, for video coding applications, adaptive frame skip is executed at the encoder side to maximize the power of MCFI so that the quality of interpolated frames is guaranteed. Experimental results show that our proposal effectively enhances the overall quality of the frame rate up-converted video sequence, both subjectively and objectively.

In this paper, we present an improved multi-pass true motion estimation algorithm for frame rate up-conversion. In the proposed motion estimation algorithm, the motion vectors of different objects which are parted with depth information will be refined respectively. Therefore, more accurate pixel-based motion vector can be obtained which causes the performance of Frame Rate Up-Conversion (FRUC). For overlapped regions, the motion vector can be chosen in candidates with depth information. Experimental results show that our proposed algorithm effectively enhances the overall quality of the frame rate up-converted video sequence, both subjectively and objectively, even with complex scenes and fast motion.