Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

The Effective of Image Retrieval in Jpeg Compressed Domain

We propose a new method of feature extraction in orderto improve the effective of image retrieving by using apartial Joint Photographic Experts Group (JPEG)compressed images algorithm. Prior to that, we prune theimages database by pre-query step based on coloursimilarity, in order to eliminate image candidates. Ourfeature extraction can be carried out directly to JPEGcompressed images. We extract two features of DCTcoefficients, DC feature and AC feature, from a JPEGcompressed image. Then we compute the Euclideandistances between the query image and the images in adatabase in terms of these two features. The image querysystem will give each retrieved image a rank to define itssimilarity to the query image. Moreover, instead of fullydecompressing JPEG images, our system only needs to dopartial entropy decoding. Therefore, our proposed schemecan accelerate the effectiveness of retrieving images.According to our experimental results, our system is notonly highly effective but is also capable of performingsatisfactoril

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

High Dynamic Range Images Coding: Embedded and Multiple Description

The aim of this work is to highlight and discuss a new paradigm for representing high-dynamic range (HDR) images that can be used for both its coding and describing its multimedia content. In particular, the new approach defines a new representation domain that, conversely from the classical compressed one, enables to identify and exploit content metadata. Information related to content are used here to control both the encoding and the decoding process and are directly embedded in the compressed data stream. Firstly, thanks to the proposed solution, the content description can be quickly accessed without the need of fully decoding the compressed stream. This fact ensures a significant improvement in the performance of search and retrieval systems, such as for semantic browsing of image databases. Then, other potential benefits can be envisaged especially in the field of management and distribution of multimedia content, because the direct embedding of content metadata preserves the consistency between content stream and content description without the need of other external frameworks, such as MPEG-21. The paradigm proposed here may also be shifted to Multiple description coding, where different representations of the HDR image can be generated accordingly to its content. The advantages provided by the new proposed method are visible at different levels, i.e. when evaluating the redundancy reduction. Moreover, the descriptors extracted from the compressed data stream could be actively used in complex applications, such as fast retrieval of similar images from huge databases

Study of a imaging indexing technique in JPEG Compressed domain

In our computers all stored images are in JPEG compressed format even when we download an image from the internet that is also in JPEG compressed format, so it is very essential that we should have content based image indexing its retrieval conducted directly in the compressed domain. In this paper we used a partial decoding algorithm for all the JPEG compressed images to index the images directly in the JPEG compressed domain. We also compare the performance of the approaches in DCT domain and the original images in the pixel domain. This technology will prove preciously in those applications where fast image key generation is required. Image and audio techniques are very important in the multimedia applications. In this paper, we comprise an analytical review of the compressed domain indexing techniques, in which we used transform domain techniques such as Fourier transform, karhunen-loeve transform, Cosine transform, subbands and spatial domain techniques, which are using vector quantization and fractrals. So after comparing other research papers we come on the conclusion that when we have to compress the original image then we should convert the image by using the 8X8 pixels of image blocks and after that convert into DCT form and so on. So after doing research on the same concept we can divide image pixels blocks into 4X4X4 blocks of pixels. So by doing the same we can compress the original image by using the steps further

Full Resolution Image Compression with Recurrent Neural Networks

This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study "one-shot" versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%-8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.Comment: Updated with content for CVPR and removed supplemental material to an external link for size limitation