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ABSTRACT
An approach for filling-in blocks of missing data in wireless image transmission is presented in this project. When compression algorithms such as JPEG are used as part of the wireless transmission process, images are first tiled into blocks of 8x8 pixels. When such images are transmitted over fading channels, the effects of noise can destroy entire blocks of the image.
An approach for filling-in blocks of missing data in wireless image transmission is presented in this project The switch between the two schemes is done in a fully automatic fashion based on the surrounding available blocks. The performance of this method is tested for various images and combinations of lost blocks.
For our implementation, we consider PGM (Portable Gray Map) images for filling in of missing image blocks. We use Java 2, The Standard Edition for implementing the algorithm, and the holes (missing blocks) are placed randomly for testing the project.
INTRODUCTION
General purpose images are most commonly compressed by lossy JPEG. JPEG divides the image into blocks of 8 by 8 pixels and calculates a two-dimensional (2-D) discrete cosine transform (DCT), followed by quantization and Huffman encoding. In common wireless scenarios, the image is transmitted over the wireless channel block by block. Due to severe fading, we may lose an entire block, even several consecutive blocks of an image. It is also reported that average packet loss rate in a wireless environment is 3.6% and occurs in a bursty fashion. In the worst case, a whole line of image blocks might be lost. Note that JPEG uses differential encoding for storing the average (dc) value of successive pixels.
Hence, even if a single block is lost, the remaining blocks in that line (or reset interval) might be received without their correct average (dc) value. Two common techniques to make the transmission robust are forward error correction (FEC) and automatic retransmission query protocols (ARQ). Of these, FEC needs extra error correction packets to be transmitted. ARQ lowers data transmission rates and can further increase the network congestion which initially induced the packet loss. Instead, we show that it is possible to satisfactorily reconstruct the lost blocks by using the available information surrounding them.
This will result in an increase in bandwidth efficiency of the transmission. The basic idea is to first automatically classify the block as textured or structured (containing edges), and then fill-in the missing block with information propagated from the surrounding pixels. In the case of structured blocks, the inpainting algorithm in is used. We test the proposed scheme with a variety of images and simulated block losses. We also combine this approach with JPEG compression itself, where the encoder voluntarily skips blocks, and these are reconstructed at the decoder in the same fashion as in the wireless scenario. This process improves the compression ratio, at little or no quality degradation.
CHAPTER 2
DESCRIPTION OF PROBLEM
The basic objective of the analysis is to develop a logical model of the system using the procedure oriented approach. During the system analysis, the system analyst works with the user to develop a logical model of the system.
The requirement analysis is the software engineering task that bridges the gap between the system level software allocation and software design. The purpose of the system requirement specification document is to be present in a precise and easily attested manner all software requirements up on the a formal review by the administration and its users. It will satisfy the functional performance interface, design and verification requirements for the computer software to be developed as a part of the computerization of the technical information system.
2.1 Existing System:
1. The images are commonly compressed by lossy JPEG compressing format.
2. Here the images are divided into a small blocks of 8X8 pixels.
3. The compression algorithm uses the Descrete Cosine Transform (DCS) algorithm, followed by quantization and Haffman encoding
4. JPEG uses differential encoding for storing the average (dc) value of successive pixels. Hence, even if a single block is lost, the remaining blocks in that line (or reset interval) might be received without their correct average (dc) value.
5. Two common techniques to make the transmission robust are forward error correction (FEC) and automatic retransmission query protocols (ARQ). Of these, FEC needs extra error correction packets to be transmitted. ARQ lowers data transmission rates and can further increase the network congestion which initially induced the packet loss.