Energy efficient algorithms and techniques for wireless mobile clients 5b

149 Original image Linear darkening approach Power: 494.0 mW GCL: PSNR: ∞ dB MSSIM: 1.000 Power: 245.7 mW GCL: 0.5 PSNR: 14.731 dB MSSIM: 0.902 Power: 256.1 mW GCL: PSNR: ∞ dB MSSIM: 1.000 Power: 142.2 mW GCL: PSNR: ∞ dB MSSIM: 1.000 Gamma compression approach HVS based mapping Power Model based mapping Power: 246.2 mW GCL:12.3 PSNR: 15.219 dB MSSIM: 0.932 Power: 245.2 mW GCL: 2.9 PSNR: 14.311 dB MSSIM: 0.902 Power: 245.3 mW GCL: 10.0 PSNR: 15.185 dB MSSIM: 0.942 Power: 127.7 mW GCL: 0.3 PSNR: 16.143 dB MSSIM: 0.810 Power: 127.2 mW GCL: 14.4 PSNR: 17.921 dB MSSIM: 0.921 Power: 127.1 mW GCL: 1.7 PSNR: 16.929 dB MSSIM: 0.901 Power: 127.1 mW GCL: 12.5 PSNR: 17.781 dB MSSIM: 0.928 Power: 71.1 mW GCL: 4.3 PSNR: 18.826 dB MSSIM: 0.664 Power: 71.1 mW GCL: 12.9 PSNR: 20.359 dB MSSIM: 0.926 Power: 71.1 mW GCL: 5.0 PSNR: 18.976 dB MSSIM: 0.748 Power: 71.1 mW GCL: 12.9 PSNR: 20.232 dB MSSIM: 0.940 Figure 4.27. Power consumption and quality measurements The user study shows mixed results. The results after removing biased entries are shown in Figure 4.28. Normalised performance count is a ratio between total number of times an image appeared in the pair for selection and number of times it is selected as the best. For high power saving, PM based approach outperforms other approaches and for low power saving, HVS based approach performs better. This may be due to the capability of HVS approach to retain global contrast when minimal changes are made. Hence, we planned to deploy a power level adaptive approach for our cloud Preference  Count  (Normalised)   service. 0.7   Linear  Darkening   HVS  Based   0.6   Gamma  Compession   Power  Model  Based   0.5   0.4   0.3   0.2   0.1     20%   40%   60%   Power  Saved   80%   Figure 4.28. Image Transformation - User Study 4.7.3 Overall Result (Combined) We now combine the results of both text and image transformations. The tradeoffs involved in saving OLED display power are when high energy saving is required, it is clear that the key image based text/background colour transformation approach should be combined with power-model based image transformation approach to save 150 60% (and above) power while providing best possible quality. With this combination, the users experienced an additional 50ms to 100ms RTT delay in getting the contents through the cloud for up to 32 users accessing the service concurrently. The RTTs were measured while the service was run as a personal cloud service in a DELL Power Edge T610 Tower Server with a hexa-core processor, 24GB RAM and TB HDD. For saving power up to 50% it is recommended to use HVS based approach or simple darkening approach with the text transformations. The advantage of simple darkening approach is in its simplicity. With simple darkening, the users have experienced less than 50ms additional RTT delay due to lower processing latency. To support large number of users, existing powerful clouds with cluster of servers can be used. The default setting in our cloud service is 20% power saving, which generates content quality (text and images) comparable to original. 4.7.4 Summary The results show that colour transformations constrained by brand identity for texts can save significantly more energy than all the previous works while retaining the readability of the contents. In certain cases, it even improves the website’s colour schemes. For images, our scheme can save the much higher level of energy while retaining the image fidelity and keeping the distortions at minimum level when compared to previous browser dependant approaches which either makes everything look greenish [6] or saves less than 5% energy [59]. With our approaches, 60% energy can be saved for web pages with images while providing good quality. For image free web pages (most mobile web pages not have images) we can save up to 80% of energy if dark background is acceptable. 151 . pair for selection and number of times it is selected as the best. For high power saving, PM based approach outperforms other approaches and for low power saving, HVS based approach performs. content quality (text and images) comparable to original. 4.7.4 Summary The results show that colour transformations constrained by brand identity for texts can save significantly more energy than all. [6] or saves less than 5% energy [59]. With our approaches, 60% energy can be saved for web pages with images while providing good quality. For image free web pages (most mobile web pages do not