Muhammad Farooq Sabir

Unequal Power Allocation for JPEG Transmission Over MIMO Systems

With the introduction of multiple transmit and receive antennas in next generation wireless systems, real-time image and video communication are expected to become quite common, since very high data rates will become available along with improved data reliability. New joint transmission and coding schemes that explore advantages of multiple antenna systems matched with source statistics are expected to be developed. Based on this idea, we present an unequal power allocation scheme for transmission of JPEG compressed images over multiple-input multiple-output systems employing spatial multiplexing. The JPEG-compressed image is divided into different quality layers, and different layers are transmitted simultaneously from different transmit antennas using unequal transmit power, with a constraint on the total transmit power during any symbol period. Results show that our unequal power allocation scheme provides significant image quality improvement as compared to different equal power allocations schemes, with the peak-signal-to-noise-ratio gain as high as 14 dB at low signal-to-noise-ratios.

Joint Source-Channel Distortion Modeling for MPEG-4 Video

Multimedia communication has become one of the main applications in commercial wireless systems. Multimedia sources, mainly consisting of digital images and videos, have high bandwidth requirements. Since bandwidth is a valuable resource, it is important that its use should be optimized for image and video communication. Therefore, interest in developing new joint source-channel coding (JSCC) methods for image and video communication is increasing. Design of any JSCC scheme requires an estimate of the distortion at different source coding rates and under different channel conditions. The common approach to obtain this estimate is via simulations or operational rate-distortion curves. These approaches, however, are computationally intensive and, hence, not feasible for real-time coding and transmission applications. A more feasible approach to estimate distortion is to develop models that predict distortion at different source coding rates and under different channel conditions. Based on this idea, we present a distortion model for estimating the distortion due to quantization and channel errors in MPEG-4 compressed video streams at different source coding rates and channel bit error rates. This model takes into account important aspects of video compression such as transform coding, motion compensation, and variable length coding. Results show that our model estimates distortion within 1.5 dB of actual simulation values in terms of peak-signal-to-noise ratio.

A joint source-channel distortion model for JPEG compressed images

The need for efficient joint source-channel coding (JSCC) is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical JSCC schemes is a distortion model that can predict the quality of compressed digital multimedia such as images and videos. The usual approach in the JSCC literature for quantifying the distortion due to quantization and channel errors is to estimate it for each image using the statistics of the image for a given signal-to-noise ratio (SNR). This is not an efficient approach in the design of real-time systems because of the computational complexity. A more useful and practical approach would be to design JSCC techniques that minimize average distortion for a large set of images based on some distortion model rather than carrying out per-image optimizations. However, models for estimating average distortion due to quantization and channel bit errors in a combined fashion for a large set of images are not available for practical image or video coding standards employing entropy coding and differential coding. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to quantization and channel bit errors in a joint manner. Statistical modeling of important compression techniques such as Huffman coding, differential pulse-coding modulation, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal-to-noise ratio (PSNR) can be predicted within a 2-dB maximum error over a variety of compression ratios and bit-error rates. To illustrate the utility of the proposed model, we present an unequal power allocation scheme as a simple application of our model. Results show that it gives a PSNR gain of around 6.5 dB at low SNRs, as compared to equal power allocation.

A real-time embedded software implementation of a turbo encoder and soft output Viterbi algorithm based turbo decoder

Turbo codes are used for error protection, especially in wireless systems. A turbo encoder consists of two recursive systematic convolutional component encoders connected in parallel and separated by a random interleaver. A turbo decoder, which is iterative, is typically based on either a soft output Viterbi algorithm (SOVA) or a maximum a posteri (MAP) algorithm. MAP is roughly three times more computationally complex than SOVA, but provides 0.5 dB of coding gain. We implement a turbo encoder and SOVA-based turbo decoder in real-time software on a TMS320C6700 digital signal processor (DSP). The contributions of this paper are: (1) first publicly available implementation of a SOVA-based turbo decoder on a C6000 DSP (2) speedup of 162x for the encoder on a C6200 DSP and 11.7x for the decoder on a C6700 DSP over level three C compiler optimization, and (3) dataflow modeling for a turbo channel coding subsystem.

Joint Source-Channel Distortion Modelling for MPEG-4 Video

Joint source-channel coding is becoming more important for wireless multimedia transmission due to high bandwidth requirements of these multimedia sources. Design of all joint source-channel coding schemes require an estimate of distortion at different source coding rates and under different channel conditions. In this paper, we present one such distortion model for estimating distortion due to quantization and channel errors in a joint manner for MPEG-4 compressed video streams. This model takes into account important aspects of video compression such as transform coding, motion compensation, and variable length coding. Results show that our model estimates distortion within 1 dB of actual simulation values in terms of peak-signal-to-noise-ratio.