Frames from Jungles Dataset

First column shows the input HDR frames (PQ->Linear->RGB_709).

Second column shows the target SDR frames.

Third column shows the predicted results for HDRNet.

Fourth and fifth column shows the results for 3 dimensions using PCA and VAE respectively.

Finally we show the results using our method in the last column.

You can toggle between the predicted and the target image by clicking on the images. Moreover, you can rearrange the images in increasing or decreasing order of the metrics.

ID	Input HDR Frame	Target SDR Frame	HDRNet	PSNR 26.614	FLIP 0.221	Delta E 2000 5.262	PCA_3_dimensions	PSNR 18.421	FLIP 0.356	Delta E 2000 15.403	Var_AutoEncoder_3_latents	PSNR 27.473	FLIP 0.201	Delta E 2000 4.446	Ours	PSNR 40.918	FLIP 0.072	Delta E 2000 1.640
000510				33.740	0.092	2.230		14.273	0.584	18.995		33.936	0.101	2.182		48.425	0.043	0.808
000516				23.172	0.341	6.417		17.906	0.438	16.187		29.221	0.209	3.822		44.255	0.068	0.898
000519				21.293	0.368	7.421		16.304	0.506	15.378		26.630	0.239	4.503		38.854	0.083	1.223
000522				21.983	0.258	7.224		17.409	0.393	12.201		24.730	0.226	5.803		37.305	0.079	1.816
000525				29.846	0.163	3.123		17.534	0.464	15.095		22.068	0.277	6.232		31.256	0.130	3.342
000528				26.496	0.186	4.422		17.258	0.409	15.920		24.289	0.225	5.194		38.880	0.076	1.497
000531				26.579	0.208	4.725		16.554	0.493	16.992		27.180	0.216	5.019		41.123	0.067	1.212
000534				25.259	0.248	4.768		15.851	0.463	14.479		26.854	0.218	4.131		40.463	0.076	1.207
000537				32.659	0.108	2.618		20.806	0.249	10.600		30.268	0.150	3.182		47.073	0.044	0.862
000540				27.466	0.186	4.212		21.442	0.169	14.208		27.221	0.209	4.236		40.267	0.072	1.455
000543				22.927	0.266	6.051		19.562	0.202	14.073		25.689	0.226	4.705		36.361	0.084	1.924
000546				30.183	0.151	3.531		22.073	0.212	11.988		24.447	0.272	5.856		36.606	0.096	2.319
000549				25.027	0.230	5.562		22.207	0.182	8.985		24.818	0.238	5.503		39.326	0.065	1.539
000552				24.237	0.275	5.615		18.343	0.385	14.809		26.533	0.228	4.472		41.510	0.078	1.310
000555				32.435	0.124	3.390		23.551	0.186	13.154		32.234	0.132	2.677		46.780	0.045	0.951
000558				28.529	0.206	4.674		18.420	0.340	15.165		29.200	0.168	3.666		44.119	0.055	1.204
000561				37.839	0.068	2.197		19.743	0.287	12.239		35.601	0.103	2.378		53.590	0.035	0.839
000564				29.103	0.111	3.703		13.574	0.291	17.009		30.950	0.141	2.581		44.614	0.051	1.028
000567				18.744	0.394	9.021		14.829	0.531	17.423		20.856	0.339	7.275		33.360	0.133	2.277
000570				27.769	0.190	3.735		23.750	0.153	14.463		30.636	0.158	3.145		44.454	0.054	1.252
000573				21.792	0.316	6.936		22.508	0.224	13.076		26.127	0.227	4.901		38.554	0.085	2.142
000576				26.927	0.212	4.107		20.013	0.139	12.947		28.609	0.188	3.850		41.322	0.064	1.351
000579				28.266	0.134	4.626		20.408	0.195	16.660		29.657	0.127	2.790		41.217	0.054	1.157
000582				32.506	0.075	4.783		19.990	0.201	16.751		29.629	0.127	2.827		41.783	0.054	1.169
000585				26.470	0.190	5.550		15.510	0.497	22.751		27.232	0.202	5.319		36.776	0.070	2.252
000588				30.488	0.171	6.277		16.042	0.494	22.214		26.165	0.214	5.572		33.812	0.085	3.035
000591				24.791	0.252	4.959		17.417	0.449	19.144		27.438	0.202	4.016		42.272	0.071	1.366
000594				22.617	0.359	7.258		16.198	0.521	20.179		22.786	0.345	7.142		31.168	0.171	4.263
000597				25.662	0.188	3.980		18.105	0.366	13.446		29.363	0.116	3.436		46.144	0.038	1.268
000600				22.653	0.296	6.396		18.862	0.382	14.114		29.240	0.161	3.718		44.865	0.052	1.112
000603				34.151	0.103	2.694		18.077	0.419	16.817		30.259	0.155	3.841		52.461	0.031	0.567
000606				32.431	0.122	2.767		14.921	0.451	18.490		29.402	0.181	3.334		50.047	0.039	0.905