Scalable video/image transmission using rate compatible PUM turbo codes

The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and coexistence. The proposed approach exploits the joint optimization of a wavelet-based scalable video/image coding framework and a forward error correction method based on PUM turbo codes. The scheme minimizes the reconstructed image/video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the rate optimization technique and the statistics of the transmission channel

Generative Compression

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression, the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at much deeper compression levels for both image and video data. We also demonstrate that generative compression is orders-of-magnitude more resilient to bit error rates (e.g. from noisy wireless channels) than traditional variable-length coding schemes

Minimum Distortion Variance Concatenated Block Codes for Embedded Source Transmission

Some state-of-art multimedia source encoders produce embedded source bit streams that upon the reliable reception of only a fraction of the total bit stream, the decoder is able reconstruct the source up to a basic quality. Reliable reception of later source bits gradually improve the reconstruction quality. Examples include scalable extensions of H.264/AVC and progressive image coders such as JPEG2000. To provide an efficient protection for embedded source bit streams, a concatenated block coding scheme using a minimum mean distortion criterion was considered in the past. Although, the original design was shown to achieve better mean distortion characteristics than previous studies, the proposed coding structure was leading to dramatic quality fluctuations. In this paper, a modification of the original design is first presented and then the second order statistics of the distortion is taken into account in the optimization. More specifically, an extension scheme is proposed using a minimum distortion variance optimization criterion. This robust system design is tested for an image transmission scenario. Numerical results show that the proposed extension achieves significantly lower variance than the original design, while showing similar mean distortion performance using both convolutional codes and low density parity check codes.Comment: 6 pages, 4 figures, In Proc. of International Conference on Computing, Networking and Communications, ICNC 2014, Hawaii, US

Embedding Authentication and DistortionConcealment in Images – A Noisy Channel Perspective

In multimedia communication, compression of data is essential to improve transmission rate, and minimize storage space. At the same time, authentication of transmitted data is equally important to justify all these activities. The drawback of compression is that the compressed data are vulnerable to channel noise. In this paper, error concealment methodologies with ability of error detection and concealment are investigated for integration with image authentication in JPEG2000.The image authentication includes digital signature extraction and its diffusion as a watermark. To tackle noise, the error concealment technologies are modified to include edge information of the original image.This edge_image is transmitted along with JPEG2000 compressed image to determine corrupted coefficients and regions. The simulation results are conducted on test images for different values of bit error rate to judge confidence in noise reduction within the received images

Robust Transmission of Images Based on JPEG2000 Using Edge Information

In multimedia communication and data storage, compression of data is essential to speed up the transmission rate, minimize the use of channel bandwidth, and minimize storage space. JPEG2000 is the new standard for image compression for transmission and storage. The drawback of Compression is that compressed data are more vulnerable to channel noise during transmission. Previous techniques for error concealment are classified into three groups depending on the Approach employed by the encoder and decoder: Forward Error Concealment, Error Concealment by Post Processing and Interactive Error Concealment. The objective of this thesis is to develop a Concealment methodology that has the capability of both error detection and concealment, be Compatible with the JPEG2000 standard, and guarantees minimum use of channel bandwidth. A new methodology is developed to detect corrupted regions/coefficients in the received Images the edge information. The methodology requires transmission of edge information of wavelet coefficients of the original image along with JPEG2000 compressed image. At the receiver, the edge information of received wavelet coefficients is computed and compared with the received edge information of the original image to determine the corrupted coefficients. Three methods of concealment, each including a filter, are investigated to handle the corrupted regions/coefficients. MATLAB™ functions are developed that simulate channel noise, image transmission Using JPEG2000 standard and the proposed methodology. The objective quality measure such as Peak-signal-to-noise ratio (PSNR), root-mean-square error (rms) and subjective quality Measure are used to evaluate processed images. The simulation results are presented to demonstrate The performance of the proposed methodology. The results are also compared with recent approaches Found in the literature. Based on performance of the proposed approach, it is claimed that the Proposed approach can be successfully used in wireless and Internet communications

Deep Joint Source-Channel Coding for Wireless Image Transmission

We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the “cliff effect,” and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values