Evolution of the discrete cosine transform using genetic programming

Compression of 2 dimensional data is important for the efficient transmission, storage and manipulation of Images. The most common technique used for lossy image compression relies on fast application of the Discrete Cosine Transform (DCT). The cosine transform has been heavily researched and many efficient methods have been determined and successfully applied in practice; this paper presents a novel method for evolving a DCT algorithm using genetic programming. We show that it is possible to evolve a very close approximation to a 4 point transform. In theory, an 8 point transform could also be evolved using the same technique

Design and Implementation of Image Compression Encoder using Orthogonal Approximation DCT

Image Compression is usually carried out using discrete cosine transform (DCT) because compressed image using DCT will take less memory to store the image and quality of the image will be good compared JPEG and HEVC. But, in this work an attempt is made to achieve compression using Approximation DCT (ADCT). ADCT is useful for reducing its computational complexity without affecting its coding performance. It provides better image and video compression compared to the DCT. ADCT is orthogonal and it has lower structural complexity compared to DCT. The unique feature of the ADCT is that it could be configured for the computation of the 32 point ADCT or for parallel computation of two16 point ADCTs or four 8 points ADCTs. It has many advantages in terms of orthogonality, structural simplicity and lower computational complexity. The proposed ADCT is implemented using Verilog and Simulated by ModelSim and synthesized by Xilinx ISE 9.1i. Results are compared with 16 point ADCT with 16 point DCT implementation. The target device is XC5vtx330t-2ff1738. The 16 point ADCT implementation results in a saving of 28.37% IOBs and 63% of LUTs, compared to existing 16 point DCT implementation

Proposed data compression schemes for the Galileo S-band contingency mission

The Galileo spacecraft is currently on its way to Jupiter and its moons. In April 1991, the high gain antenna (HGA) failed to deploy as commanded. In case the current efforts to deploy the HGA fails, communications during the Jupiter encounters will be through one of two low gain antenna (LGA) on an S-band (2.3 GHz) carrier. A lot of effort has been and will be conducted to attempt to open the HGA. Also various options for improving Galileo's telemetry downlink performance are being evaluated in the event that the HGA will not open at Jupiter arrival. Among all viable options the most promising and powerful one is to perform image and non-image data compression in software onboard the spacecraft. This involves in-flight re-programming of the existing flight software of Galileo's Command and Data Subsystem processors and Attitude and Articulation Control System (AACS) processor, which have very limited computational and memory resources. In this article we describe the proposed data compression algorithms and give their respective compression performance. The planned image compression algorithm is a 4 x 4 or an 8 x 8 multiplication-free integer cosine transform (ICT) scheme, which can be viewed as an integer approximation of the popular discrete cosine transform (DCT) scheme. The implementation complexity of the ICT schemes is much lower than the DCT-based schemes, yet the performances of the two algorithms are indistinguishable. The proposed non-image compression algorith is a Lempel-Ziv-Welch (LZW) variant, which is a lossless universal compression algorithm based on a dynamic dictionary lookup table. We developed a simple and efficient hashing function to perform the string search