ABSTRACT
Research attention has been focussed on the reduction of image data size (major problem) for its efficient compression, storage, and transmission. In this work, the developed brightness enhancement model was used to enhance the Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Lifting Wavelet Transform (LWT), and Firefly Optimization Algorithm (FOA) compression, which were then used to compress the six video data. In the pre-processing stage, the video data were converted into frames of pictures for easy analysis. The hue and saturation were then extracted and the images were noised and filtered using MATLAB R2014b simulation environment. Based on the analysis of random variation of pixel intensity and histogram distribution, the developed brightness enhancement technique was used to improve frame signal as a result of loss during compression. The performances of various enhanced compression techniques were evaluated through a number of MATLAB R2014b simulations using Peak signal to noise ratio(PSNR) as a performance metric. The results showed that the PSNR values for the grey level (black and white) images were improved by 31.95dB and 22.30dB for NAERLS1.avi and NAERLS2.aviwhen subjected to brightness enhancement technique. Also, PSNR improvements of 17.71dB and 23.31dB were obtained for the NTA1.avi and NTA2.avi, respectively, as well as15.06dB and 19.17dB improvements were obtained for the Foreman.avi and Akiyo.avi benchmark samples respectively. Similarly, improvement in terms of PSNR was also registered when coloured images were subjected to the developed brightness enhancement technique. The research implemented four video compression techniques DCT, DWT, LWT, and FOA compression, which were used as benchmarks for the developed modified FOA (mFOA)compression technique. Their respective outputs were improved using the developed brightness enhancement model in order to account for the loss of signal quality which might have occurred during compression. PSNR simulation results showed that them FOA compression technique performed better than DCT, DWT, LWT, and FOA compression techniques. For example, before enhancement, it was found that the mFOAPSNR result was better than the LWT by 73.64%, 80.04%, 80.03%, and 80.40%,respectively for NAERLS1.avi, NAERLS2.avi, NTA1.avi and NTA2.avi captured video frames and an improvement of 75.78% and 77.56% for Akiyo.avi and Forman.avi benchmark video frames. The mFOA was also discovered to outperform the FOA by 7.34%, 3.30%, 4.90%, and 5.75% for NAERLS1.avi, NAERLS2.avi, NTA1.avi and NTA2.avi captured video frames before enhancement and an improvement of 3.56% and 3.86% for Akiyo.avi and Forman.avi benchmark video frames. Similarly, the enhanced mFOA (E-mFOA) compression technique also produced PSNR improvement of 72.09%, 79.04%, 79.51% and 78.81% over enhanced LWT (E-LWT) for NAERLS1.avi, NAERLS2.avi, NTA1.avi and NTA2.avi capture video frames and an improvement of 74.67% and 76.08% for Akiyo.avi and Forman.avi benchmark video frames. The E-mFOA compression technique also produced a better PSNR improvement of 4.59%, 1.14%, 2.08%, and 1.17% over E-FOA for NAERLS1.avi, NAERLS2.avi, NTA1.avi and NTA2.avi captured video frames, except for the Akiyo.avi and Forman.avi benchmark video frames, where an insignificant improvement of 0.41% and -0.06% were registered. These might have been as a result of the low level of light present when the video clips were taken
