研究生: |
李家安 Chia-An Lee |
---|---|
論文名稱: |
視覺評估方法進行頭戴式顯示器之色彩特性描述 Characterising a Head-Mounted Display Based on Visual Assessment |
指導教授: |
歐立成
Li-Chen Ou |
口試委員: |
孫沛立
Pei-Li Sun 林宗翰 Tzung-Han Lin |
學位類別: |
碩士 Master |
系所名稱: |
應用科技學院 - 色彩與照明科技研究所 Graduate Institute of Color and Illumination Technology |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 155 |
中文關鍵詞: | 虛擬實境 、視覺評估 、色彩特性描述 |
外文關鍵詞: | Virtual reality, Visual assessment, Colour characterisation |
相關次數: | 點閱:373 下載:4 |
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虛擬實境(Virtual reality, VR)和相關載體如頭戴式顯示器(Head-mounted display, HMD)的應用日益增多。但對於 VR 色彩的相關研究仍然相對有限,其 中一個主要原因是 HMD 等 VR 影像呈現設備,在色彩量測方面存在著許多限 制,傳統的色彩量測方法無法完全適用於評估 HMD 或 VR 色彩的特性,而專用 的色彩量測儀器又成本高昂且難以取得。因此,為解決 HMD 的 VR 色彩之量測 和色彩特性化問題,本研究提出一種色彩特性描述方法,不使用色彩量測儀器 來量測 HMD 的 VR 色彩,而是進行一系列視覺評估實驗,並以視覺評估實驗結 果為基礎,建構 HMD 的色彩特性描述模型,實現對 HMD 中 VR 色彩的預測。
本研究共進行三個實驗:(一)LCD 顯示器視覺評估實驗、(二)HMD 視覺評估實驗、(三)HMD 色外貌實驗。其中,實驗一與實驗二的視覺評估, 均包含了分段調配(Partition scaling)、獨特色相選擇(Unique hue selection) 等視覺評估方法,此外實驗二還包括了對色(Colour matching)之視覺評估。 本研究採用 GOG(Gain-offset-gamma)模型架構進行顯示器色彩特性描述,分 段調配、獨特色相選擇、對色實驗這三種視覺評估方法,都是為了獲取建構 GOG 模型所需之輸入參數,以上述視覺評估方法之結果建置的色彩特性描述模 型,稱為「視覺評估 GOG 模型」與「明度評估 GOG 模型」。本研究也使用相 同的 HMD 進行實驗三的色外貌實驗,驗證模型的預測表現。
實驗結果顯示,該「視覺評估 GOG 模型」在預測色相(Hue)上表現最為 準確,且使用 CIECAM02 和 CIECAM16 的預測值都相當接近;明度(Lightness) 和視彩度(Colorfulness)的預測值則會低估。其中,CIECAM02 的明度預測值 和 CIECAM16 的視彩度預測較接近色外貌實驗結果。明度和視彩度的模型預測 值會低估,可能是由於 HMD 的顯示的亮度(Luminance)過高和模型缺乏彩度 相關的最佳化目標值所致。
本研究為 VR 色彩研究提供了一種使用視覺評估實驗建構 HMD「視覺評估 GOG 模型」的方法,並且研究結果顯示該模型對於 VR 色彩中的色相預測最為 準確、在明度和視彩度預測上則有高相關性。
Applications of virtual reality and related devices, such as head-mounted displays (HMDs), are increasing. However, interest of research on the colour science of VR is still lower than expected. One of the reasons is the limitation of colour measurement techniques for HMDs. The specific colour measuring instruments for near-eye devices are expensive. And whether the data of VR colour measured by general colour measuring instruments can represent the characterisation of HMDs or VR colours is a problem. Therefore, in this study, a method for characterising HMD colour by visual assessment was developed. By carrying out a series of visual assessment experiments, colour characterisation models were established without measuring HMDs directly.
There were three experiments conducted: (1) a visual assessment experiment on an LCD; (2) a visual assessment experiment on an HMD; (3) a colour appearance experiment using the HMD. Experiments (1) and (2) both involved two visual assessment methods, partition scaling and unique hue selection, while experiment (2) also included a color matching technique. The visual-assessment colour characterising model in this study were based on the GOG (Gain-offset-gamma) model. The partition scaling, unique hue selection, and colour matching techniques were used to obtain input parameters constructing GOG models. The GOG models built by methods mentioned above are called “visual assessment GOG model” and “lightness assessment GOG model”. The same HMD that adopted in experiment (2) was continuedly used in rear experiment (3).
The results show that visual assessment GOG models perform most accurately in predicting hue, and the predicted hue values from CIECAM02 and CIECAM16 are similar. However, the prediction of lightness and colorfulness are underestimated. In the comparison, predicted lightness value from CIECAM02 and predicted colorfulness value from CIECAM16 are slightly closer to the results of the colour appearance experiment. The underestimation of lightness and colorfulness by visual assessment models may be attributed to the high luminance of the HMD and the lack of optimal target values related to chroma or colorfulness in the models
This study provides a method for building visual assessment GOG models for HMDs in VR color research area, and the results indicate that the model accurately predicts hue in VR colors, while showing high correlation in predicting lightness and colorfulness.
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