Update README.md

README.md

@@ -22,44 +22,15 @@ configs:
 
 SAVAM contains human eye-movement recordings collected while viewing videos, including static and dynamic scenes, film excerpts, and sequences from research video databases.
 
-## Dataset viewer
-
-Choose a subset in the viewer above:
-
-| Subset | One row represents | Contents |
-| --- | --- | --- |
-| **`clips`** (default) | A video clip | A source frame, gaze-point overlay and saliency map from the middle of the clip; video dimensions, frame rate, duration, recording count and links to the original videos |
-| **`filtered_gaze`** | A filtered gaze sample | Timestamp and left/right eye coordinates, together with clip, observer and recording metadata |
-| **`raw_gaze`** | An original gaze sample | The corresponding unfiltered measurements and recording metadata |
-
-This uploaded copy contains **41 clips** (`v01`–`v39`, `v42`, `v43`) and **2,378 recordings** in each gaze subset. The official collection description below reports 43 clips; `v40` and `v41` are not present in the uploaded source files.
-
-The three images in each clip row are reduced-size snapshots at `preview_time_s`. Full videos are available through the `source_left_video`, `source_right_video`, `gaze_overlay_video` and `saliency_video` columns. Video dimensions and frame rates describe the original source videos.
-
-The gaze tables preserve every numeric sample and zero coordinate from the original TXT files. Coordinates use `float64`; `timestamp_us` uses `int64` and retains the original timestamp. `recording_id` is the complete original filename without `.txt`, including observer, age, sex, trial and presentation order. All original files remain available in [Files and versions](https://huggingface.co/datasets/ANDRYHA/SAVAM/tree/main).
-
-The `train` split is a storage convention for browsing all available records; it does **not** define an official training/evaluation partition.
-
-```python
-from datasets import load_dataset
-
-clips = load_dataset("ANDRYHA/SAVAM", "clips", split="train")
-gaze = load_dataset("ANDRYHA/SAVAM", "filtered_gaze", split="train", streaming=True)
-```
-
 ## Official dataset description
 
-The [official project page](https://videoprocessing.ai/datasets/savam.html) reports:
-
-- 43 video fragments from feature films, commercials, and stereoscopic video databases.
+- 43 video fragments (41 available) from feature films, commercials, and stereoscopic video databases.
 - Approximately 13 minutes of video, comprising 19,760 frames.
 - 50 observers, predominantly aged 18–27.
 - Full HD and 4K UHDTV stereoscopic video sequences.
 - Eye tracking with an SMI iViewX Hi-Speed 1250 at 500 Hz.
 - Additional postprocessing to improve recording accuracy.
 
-Only the left video view was shown to observers. These figures describe the collection reported on the official project page.
-
 ## Gaze data
 
 The descriptions below follow the original [`GazeData/README.txt`](GazeData/README.txt).
@@ -70,7 +41,7 @@ The descriptions below follow the original [`GazeData/README.txt`](GazeData/READ
 | `GazeData/filtered_gaze_data/` | Filtered eye-tracking data |
 | `GazeData/gaussian_vizualizations/` | Gaze-location distributions visualized with multiple Gaussians |
 
-The authors describe their processing procedure in the [Data post-processing section](https://videoprocessing.ai/datasets/savam.html#data-post-processing) of the official page.
+The original processing procedure described in the [Data post-processing section](https://videoprocessing.ai/datasets/savam.html#data-post-processing) of the official page.
 
 ### Metadata
 
@@ -86,17 +57,17 @@ The original format documentation lists the following fields:
 
 | Position | Field |
 | --- | --- |
-| 1 | Timestamp; 1,000,000 units correspond to approximately one second |
+| 1 | Timestamp; 1,000,000 units correspond to one second |
 | 2 | Left X coordinate, documented range 0–1920 |
 | 3 | Left Y coordinate, documented range 0–1080 |
 | 4 | Right X coordinate, documented range 0–1920 |
 | 5 | Right Y coordinate, documented range 0–1080 |
 
-If both X and Y are zero, the gaze position is unknown. According to the original documentation, this usually means the observer's eyes were closed.
+If both X and Y are zero, the gaze position is unknown. This usually means the observer's eyes were closed.
 
-## Original license files
+## License files
 
-[`GazeData/LICENSE.txt`](GazeData/LICENSE.txt) specifies Creative Commons Attribution 4.0 International and requests citation of the SAVAM paper.
+[`GazeData/LICENSE.txt`](GazeData/LICENSE.txt) specifies Creative Commons Attribution 4.0 International for gaze data and requests citation of the SAVAM paper.
 
 The original video notices are available here:
 
@@ -113,8 +84,23 @@ The original video notices are available here:
 
 ## Citation
 
-The [official dataset page](https://videoprocessing.ai/datasets/savam.html#reference) provides the following reference:
-
-Y. Gitman, M. Erofeev, D. Vatolin, A. Bolshakov, A. Fedorov. “Semiautomatic Visual-Attention Modeling and Its Application to Video Compression”. 2014 IEEE International Conference on Image Processing (ICIP). Paris, France, pp. 1105–1109.
+```
+ @INPROCEEDINGS {
+    Gitm1410:Semiautomatic,
+    AUTHOR    = "Yury Gitman and Mikhail Erofeev and Dmitriy Vatolin
+                 and Andrey Bolshakov and Alexey Fedorov",
+    TITLE     = "Semiautomatic {Visual-Attention} Modeling and Its 
+                 Application to Video Compression",
+    BOOKTITLE = "2014 IEEE International Conference on Image Processing
+                 (ICIP) (ICIP 2014)",
+    ADDRESS   = "Paris, France",
+    PAGES     = "1105-1109",
+    DAYS      =  27,
+    MONTH     =  oct,
+    YEAR      =  2014,
+    KEYWORDS  = "Saliency;Visual attention;Eye-tracking;Saliencyaware 
+                 compression;H.264",
+  }
+```
 
-[Accepted manuscript (PDF)](https://compression.ru/video/savam/pdf/Semiautomatic_visual_attention_modeling_and_its_application_to_video_compression.pdf) · [Published paper (IEEE)](https://ieeexplore.ieee.org/document/7025220)
+[Accepted manuscript (PDF)](https://compression.ru/video/savam/pdf/Semiautomatic_visual_attention_modeling_and_its_application_to_video_compression.pdf) · [Published paper (IEEE)](https://ieeexplore.ieee.org/document/7025220)