DCT-based video downscaling transcoder using split and merge technique

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

An Efficient Motion Estimation Method for H.264-Based Video Transcoding with Arbitrary Spatial Resolution Conversion

As wireless and wired network connectivity is rapidly expanding and the number of network users is steadily increasing, it has become more and more important to support universal access of multimedia content over the whole network. A big challenge, however, is the great diversity of network devices from full screen computers to small smart phones. This leads to research on transcoding, which involves in efficiently reformatting compressed data from its original high resolution to a desired spatial resolution supported by the displaying device. Particularly, there is a great momentum in the multimedia industry for H.264-based transcoding as H.264 has been widely employed as a mandatory player feature in applications ranging from television broadcast to video for mobile devices. While H.264 contains many new features for effective video coding with excellent rate distortion (RD) performance, a major issue for transcoding H.264 compressed video from one spatial resolution to another is the computational complexity. Specifically, it is the motion compensated prediction (MCP) part. MCP is the main contributor to the excellent RD performance of H.264 video compression, yet it is very time consuming. In general, a brute-force search is used to find the best motion vectors for MCP. In the scenario of transcoding, however, an immediate idea for improving the MCP efficiency for the re-encoding procedure is to utilize the motion vectors in the original compressed stream. Intuitively, motion in the high resolution scene is highly related to that in the down-scaled scene. In this thesis, we study homogeneous video transcoding from H.264 to H.264. Specifically, for the video transcoding with arbitrary spatial resolution conversion, we propose a motion vector estimation algorithm based on a multiple linear regression model, which systematically utilizes the motion information in the original scenes. We also propose a practical solution for efficiently determining a reference frame to take the advantage of the new feature of multiple references in H.264. The performance of the algorithm was assessed in an H.264 transcoder. Experimental results show that, as compared with a benchmark solution, the proposed method significantly reduces the transcoding complexity without degrading much the video quality

Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena

Steered mixture-of-experts for light field images and video : representation and coding

Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images. However, most images on the internet have not been manipulated and any automated manipulation detection algorithm must carefully control the false alarm rate. In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis. The automated procedure consists of three primary steps. First, resampling features are calculated for image blocks. A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled. We use a-contrario hypothesis testing to both identify if the patterns of the manipulated blocks indicate if the image has been tampered with and to localize the manipulation. We demonstrate that this strategy is effective in indicating if an image has been manipulated and localizing the manipulations.Comment: arXiv admin note: text overlap with arXiv:1802.0315

Efficient Learning-based Image Enhancement : Application to Compression Artifact Removal and Super-resolution

Many computer vision and computational photography applications essentially solve an image enhancement problem. The image has been deteriorated by a specific noise process, such as aberrations from camera optics and compression artifacts, that we would like to remove. We describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adapt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to example enhancement applications including single-image super-resolution, as well as artifact removal in JPEG- and JPEG 2000-encoded images