Compressed Domain Techniques Research Articles

Survey of Compressed Domain Video Summarization Techniques

Video summarization is the method of extracting key frames or clips from a video to generate a synopsis of the content of the video. Generally, video is compressed before storing or transmitting it in most of the practical applications. Traditional techniques require the videos to be decoded to summarize them, which is a tedious job. Instead, compressed domain video processing can be used for summarizing videos by partially decoding them. A classification and analysis of various summarization techniques are presented in this article with special focus on compressed domain techniques along with a discussion on machine-learning-based techniques that can be applied to summarize the videos.<?vsp -1.2pt?>

A review on document image analysis techniques directly in the compressed domain

The rapid growth of digital libraries, e-governance, and internet based applications has caused an exponential escalation in the volume of ‘Big-data’ particularly due to texts, images, audios and videos that are being both archived and transmitted on a daily basis. In order to make their storage and transfer efficient, different data compression techniques are used in the literature. The ultimate motive behind data compression is to transform a big size data into small size data, which eventually implies less space while archiving, and less time in transferring. However, in order to operate/analyze compressed data, it is usually necessary to decompress it, so as to bring back the data to its original form, which unfortunately warrants an additional computing cost. In this backdrop, if operating upon the compressed data itself can be made possible without going through the stage of decompression, then the advantage that could be accomplished due to compression would escalate. Further due to compression, from the data structure and storage perspectives, the original visibility structure of the data also being lost, it turns into a potential challenge to trace the original information in the compressed representation. This challenge is the motivation behind exploring the idea of direct processing on the compressed data itself in the literature. The proposed survey paper specifically focuses on compressed document images and brings out two original contributions. The first contribution is that it presents a critical study on different image analysis and image compression techniques, and highlights the motivational reasons for pursuing document image analysis in the compressed domain. The second contribution is that it summarizes the different compressed domain techniques in the literature so far based on the type of compression and operations performed by them. Overall, the paper aims to provide a perspective for pursuing further research in the area of document image analysis and pattern recognition directly based on the compressed data.

Fast retrieval of color objects with multidimensional orthogonal polynomials

In this paper, a color model for the Orthogonal Polynomials Transform and modifications to the generating function of the transform's coefficients in order to enhance the speed of the transform have been proposed. Utilizing this simple and integer-based transform, a fast color image annotation and retrieval system is proposed. In the proposed retrieval system, images are represented as a mixture of Gaussians which are built from the transform's partial coefficients. Annotation is performed by estimating the Kullback Leibler distance between the Gaussian distributions of the query and that of the database. This retrieval system is fast owing to the following reasons: 1) Usage of a computationally light transform 2) Sufficiency of partial decoding of the transform's coefficients for building the image representation owing to its energy compaction property 3) Exploitation of the inherent symmetry of the point spread operator of transform which is useful for fast determination of the transform's coefficients and 4) Non-usage of any time consuming weight assignment algorithm while fusing multiple features into the feature vector. The algorithm is validated on the COIL-100 database which has been categorized into six types for the purpose of analyzing the results better. An optimum number of extracted features and Gaussian mixtures that give a good annotation and retrieval performance is determined. The performance of the proposed system is compared with that of other recent compressed domain techniques and also with the feature set given by the local descriptors of SIFT.

Compressed-Domain Techniques for Error-Resilient Video Transcoding Using RPS

In video applications where video sequences are compressed and stored in a storage device for future delivery, the encoding process is typically carried out without enough prior knowledge about the channel characteristics of a network. Error-resilient transcoding plays an important role to provide an addition of resilience to the video data, where or whenever it is needed. Recently, a reference picture selection (RPS) scheme has been adopted in an error-resilient transcoder in order to reduce error effects for the already encoded video bitstream. In this approach, the transcoder learns through a feedback channel about the damaged parts of a previously coded frame and then decides to code the next P-frame not relative to the most recent, but to an older, reference picture, which is known to be error-free in the decoder. One straightforward approach of adopting RPS in error-resilient transcoding is to decode all the P-frames from the previously nearest I-frame to the current transmitted frame which is then re-encoded with a new reference frame; this can create undesirable complexity in the transcoder as well as introduce re-encoding errors. In this paper, some novel techniques are suggested for an effective implementation of RPS in the error-resilient transcoder with the minimum requirement on its complexity. All the proposed techniques will manipulate video data in the compressed domain such that the computational loading of the transcoder is greatly reduced. By utilizing these new compressed-domain techniques, we develop a new structure to handle various types of macroblocks in the transcoder which re-uses motion vectors and prediction errors from the encoded bitstream. Experimental results demonstrate that significant improvements in terms of transcoder complexity and quality of reconstructed video can be achieved by employing our compressed-domain techniques.

New Architecture for MPEG Video Streaming System With Backward Playback Support

MPEG digital video is becoming ubiquitous for video storage and communications. It is often desirable to perform various video cassette recording (VCR) functions such as backward playback in MPEG videos. However, the predictive processing techniques employed in MPEG severely complicate the backward-play operation. A straightforward implementation of backward playback is to transmit and decode the whole group-of-picture (GOP), store all the decoded frames in the decoder buffer, and play the decoded frames in reverse order. This approach requires a significant buffer in the decoder, which depends on the GOP size, to store the decoded frames. This approach could not be possible in a severely constrained memory requirement. Another alternative is to decode the GOP up to the current frame to be displayed, and then go back to decode the GOP again up to the next frame to be displayed. This approach does not need the huge buffer, but requires much higher bandwidth of the network and complexity of the decoder. In this paper, we propose a macroblock-based algorithm for an efficient implementation of the MPEG video streaming system to provide backward playback over a network with the minimal requirements on the network bandwidth and the decoder complexity. The proposed algorithm classifies macroblocks in the requested frame into backward macroblocks (BMBs) and forward/backward macroblocks (FBMBs). Two macroblock-based techniques are used to manipulate different types of macroblocks in the compressed domain and the server then sends the processed macroblocks to the client machine. For BMBs, a VLC-domain technique is adopted to reduce the number of macroblocks that need to be decoded by the decoder and the number of bits that need to be sent over the network in the backward-play operation. We then propose a newly mixed VLC/DCT-domain technique to handle FBMBs in order to further reduce the computational complexity of the decoder. With these compressed-domain techniques, the proposed architecture only manipulates macroblocks either in the VLC domain or the quantized DCT domain resulting in low server complexity. Experimental results show that, as compared to the conventional system, the new streaming system reduces the required network bandwidth and the decoder complexity significantly.

Video Segmentation in the Wavelet Compressed Domain

Automatic video indexing is an important feature in the design of a video database. Recently, compressed domain techniques have become popular due to their inherent advantages of efficiency and lower complexity. On the other hand, wavelet transform has emerged as a powerful tool for efficient compression of visual information. A variety of wavelet-based video compression techniques have been reported in the literature. However, there has been little work done in the area of video indexing in the wavelet domain. In this paper, we present video segmentation techniques in a wavelet-based compression framework. These techniques employ wavelet coefficients, their distribution, and the motion vectors estimated by the associated video coder. Simulation results show that the proposed techniques provide a good indexing performance at a low complexity.

Image/video spatial scalability in compressed domain

Image/video compression is widely used in various applications with the advent of the Joint Photographic Expert Group (JPEG), Motion Picture Expert Group (MPEG), and H.261 standards. Hence, most visual data are stored in the compressed format. Spatial scalable encoding of visual data has several applications, including browsing visual databases, querying multimedia databases, interactive multimedia communications, etc. We propose two novel techniques, namely, format compatible (FC) discrete cosine transform (DCT) and format modified (FM) DCT to implement image/video spatial scalability directly in the DCT compressed domain. The FC-DCT technique can be used to manipulate the standard bit streams, such as JPEG, MPEG, etc., while the FM-DCT technique can be employed in a variety of applications which require fast processing. In contrast to the traditional spatial-domain techniques, the compressed-domain techniques remove the unnecessary decompression and recompression procedures, thus, they have the advantages of reduced computational complexity and storage requirements. Simulation results demonstrate that the proposed DCT-domain techniques can achieve a comparable performance at a much lower computational complexity compared to the spatial-domain techniques.