Residual frame

About: Residual frame is a research topic. Over the lifetime, 4443 publications have been published within this topic receiving 68784 citations.

Method and apparatus for adaptive encoding framed data sequences

Abstract: Methods and apparatus for adaptive encoding of at least a part of a current frame of a sequence of frames of framed data are described which operate on a block-by-block coding basis. The methods and apparatus divide at least a part of the current frame into blocks and then perform a first sub-encoding step on a block. Thereafter a second sub-encoding step is performed on the first sub-encoded block whereby the second sub-encoding step is optimized by adapting its encoding parameters based on a quantity of the first sub-encoded part of the current frame. The quantity is determined by prediction from a reference frame. Then the same steps are performed on another block of the part of the current frame. Typically, the framed data will be video frames for transmission over a transmission channel. The adaptation of the parameters for the second sub-encoding step may be made dependent upon the characteristics or limitations, e.g. bandwidth limitation, of the channel. In addition, the current frame may be discarded based on the predicted quantity and/or based on fullness of a buffer.

Key Frame Estimation in Video Using Randomness Measure of Feature Point Pattern

Abstract: In this paper, a generalized statistical tool is introduced to estimate key frames in a video sequence. The tool works based on the inter-relationship between different features of image frames in a video. The image feature vectors are plotted in feature space as points and a randomness measure is determined from the distribution of these points. The randomness measure of the feature vectors is defined with respect to simulated random point patterns and expressed as a probability value of a frame being a key frame. Since, depending on the video content more than one inter-relationship of features can be used to determine a single key frame, different probability values are derived to support a frame as a key frame. To integrate these probability values a combiner model is designed to uniquely decide the status of a key frame. The combiner model is based on the Dempster-Shafer theory of evidence. To demonstrate the idea, randomness measures, and consequently the probabilities of a frame to be a key frame, are obtained separately from spatial domain and frequency domain features. The combined probability value enhances the confidence in selecting a frame as a key frame. The result is tested on a number of standard video sequences and it outperforms the related approach

Video coding with dynamic background

Abstract: Motion estimation (ME) and motion compensation (MC) using variable block size, sub-pixel search, and multiple reference frames (MRFs) are the major reasons for improved coding performance of the H.264 video coding standard over other contemporary coding standards. The concept of MRFs is suitable for repetitive motion, uncovered background, non-integer pixel displacement, lighting change, etc. The requirement of index codes of the reference frames, computational time in ME & MC, and memory buffer for coded frames limits the number of reference frames used in practical applications. In typical video sequences, the previous frame is used as a reference frame with 68–92% of cases. In this article, we propose a new video coding method using a reference frame [i.e., the most common frame in scene (McFIS)] generated by dynamic background modeling. McFIS is more effective in terms of rate-distortion and computational time performance compared to the MRFs techniques. It has also inherent capability of scene change detection (SCD) for adaptive group of picture (GOP) size determination. As a result, we integrate SCD (for GOP determination) with reference frame generation. The experimental results show that the proposed coding scheme outperforms the H.264 video coding with five reference frames and the two relevant state-of-the-art algorithms by 0.5–2.0 dB with less computational time.

Method and apparatus for video on demand

Abstract: An exemplary method for processing and outputting video frames includes receiving a stream of video frames, inserting geo-location data into a video frame to generate a modified video frame (704), and outputting the modified video frame. An exemplary system includes an antenna which receives a stream of video frames, a processor which inserts geo-location data into a video frame to generate a modified video frame, and an output for outputting the modified video frame.

Driving device of an image display device, program and storage medium thereof, image display device, and television receiver

Abstract: A noise adding circuit adds noise data to video data, and a circuit rounds a less significant bit, so as to output video data of 6 bits from 8 bit input data for example. The video data of 6 bits is stored in a frame memory until a further next frame, and a previous frame grayscale correction circuit corrects video data of a previous frame as required so that the video data of the previous frame approaches video data of a further previous frame. It then outputs thus corrected video data. Further, a modulation processing section corrects video data of a current frame so as to emphasize grayscale transition from the video data of the previous frame which is outputted by the previous frame grayscale correction circuit. Thus, it is possible to realize a driving device of an image display device, which can improve a response speed of pixels and has a simple arrangement, without apparently deteriorating display quality of an image displayed in the pixels.