Inter frame

About: Inter frame is a research topic. Over the lifetime, 4154 publications have been published within this topic receiving 63549 citations.

Inter mode determination method for video encoding

Abstract: The present invention relates to a method for a video encoder to determine an inter mode. The video encoder selects first search modes by using optimized inter mode information of a correlation macroblock having the same position as a current macroblock in a previous frame, in order to determine the inter mode. The video encoder compares a rate-distortion cost of the correlation macroblock and a rate-distortion cost of the mode that is selected as the minimum cost mode from among the first search modes, and determines whether to terminate an inter mode determination process early. When the early termination condition is satisfied, the video encoder determines the search mode having the minimum rate-distortion cost from among the first search modes as the optimized inter mode of the current macroblock, and terminates the inter mode determination process early. When the early termination condition is not satisfied, the video encoder selects second search modes to additionally perform an inter prediction process, and determines the corresponding search mode having the minimum rate-distortion cost as the optimized inter mode of the current macroblock.

Modeling video sources for real-time scheduling

Abstract: What is the impact of autocorrelation of variable bit rate (VBR) video sources on real-time scheduling algorithms? Our results show that the impact of long term, or interframe, autocorrelation is negligible, while the impact of short term, or intraframe, autocorrelation can be significant. Such results are essentially independent of the video coding scheme employed. To derive these results, we introduce a model that is based on statistical analysis performed on actual video data. Our model accurately captures the distribution and the autocorrelation function of the source bit stream on both the frame and the slice level. We show that the original video data sequence can be modeled as a collection of stationary subsequences called scenes. Within a scene, a model is derived for both the sequence of frames and of slices. In previous work at the slice level, the pseudo-periodicity of the autocorrelation function made it difficult, to develop a simple yet accurate model. One of the new elements introduced in this work is that we present a generalization of previous methods, that can easily capture this pseudo-periodicity and is suited for modeling a greater variety of autocorrelation functions. The generality of our model lies in that, by simply tuning a few parameters, it is able to reproduce the statistical behavior of sources with different types and levels of correlation. >

Method of temporal noise reduction in video sequences

Abstract: A motion-adaptive temporal noise reducing method and system for reducing noise in a sequence of video frames is provided. Temporal noise reduction is applied to two video frames, wherein one video frame is the current input noisy frame, and the other video frame is a previous filtered frame stored in memory. Once the current frame is filtered, it is saved into memory for filtering the next incoming frame. A motion-adaptive temporal filtering method is applied for noise reduction. Pixel-wise motion information between the current frame and the previous (filtered) frame in memory is examined. Then the pixels in the current frame are classified into motion region and non-motion region relative to the previous (filtered) frame. In a non-motion region, pixels in the current frame are filtered along the temporal axis. In a motion region, the temporal filter is switched off to avoid motion blurring.

Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos

Abstract: With the explosion in the use of cameras in mobile phones or video surveillance systems, it is impossible to transmit a large amount of videos captured from a wide area into a cloud for big data analysis and retrieval. Instead, a feasible solution is to extract and compress features from videos and then transmit the compact features to the cloud. Meanwhile, many recent studies also indicate that the features extracted from the deep convolutional neural networks will lead to high performance for various analysis and recognition tasks. However, how to compress video deep features meanwhile maintaining the analysis or retrieval performance still remains open. To address this problem, we propose a high-efficiency deep feature coding (DFC) framework in this paper. In the DFC framework, we define three types of features in a group-of-features (GOFs) according to their coding modes (i.e., I-feature, P-feature, and S-feature). We then design two prediction structures for these features in a GOF, including a sequential prediction structure and an adaptive prediction structure. Similar to video coding, it is important for P-feature residual coding optimization to make a tradeoff between feature bitrate and analysis/retrieval performance when encoding residuals. To do so, we propose a rate-performance-loss optimization model. To evaluate various feature coding methods for large-scale video retrieval, we construct a video feature coding data set, called VFC-1M, which consists of uncompressed videos from different scenarios captured from real-world surveillance cameras, with totally 1M visual objects. Extensive experiments show that the proposed DFC can significantly reduce the bitrate of deep features in the videos while maintaining the retrieval accuracy.

Scene-adaptive motion interpolation structures based on temporal masking in human visual perception

Abstract: In this paper we present a novel technique to dynamically adapt motion interpolation structures by temporal segmentation. The interval between two reference frames is adjusted according to the temporal variation of the input video. The difficulty of bit rate control for this dynamic group of pictures (GOP) structure is resolved by taking advantage of temporal masking in human vision. Six different frame types are used for efficient bit rate control, and telescopic search is used for fast motion estimation because frame distances between reference frames are dynamically varying. Constant picture quality can be obtained by variable bit rate coding using this approach and the statistical bit rate behavior of the coder is discussed. Advantages for low bit rate coding and storage media applications and implications for HDTV coding are discussed. Simulations on test video including HDTV sequences are presented for various GOP structures and different bit rates, and the results compare favorably with those for conventional fixed GOP structures.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.