int x = get_global_id(0); int y = get_global_id(1); int idx = y * tileWidth + x;
| Feature removed | Latency (ms) | PSNR (dB) | Memory (GB) | |-----------------|--------------|----------|-------------| | Tile compression | +9 | –0.3 | +6 | | Adaptive scheduler | +13 | –0.7 | – | | Lock‑free queues | +5 | –0.1 | – | MEYD-115-EN-MOSAIC-JAVHD-TODAY-1004202201-58-35...
Latency is measured from network packet receipt to final pixel output using a embedded in the RTP header and retrieved after rendering. The system maintains a sliding window of the last 1 000 measurements to compute average , p‑90 , and p‑99 values. int x = get_global_id(0); int y = get_global_id(1);
If you're looking for general information on a specific topic related to this, could you please provide more context or clarify your question? The system is validated on a public dataset
The proliferation of ultra‑high‑definition (UHD) video streams and the demand for live visual analytics have driven the need for scalable, low‑latency mosaicking solutions. This paper presents , a Java‑based high‑definition (JAVHD) video‑mosaic framework that can ingest, process, and display up to 64 concurrent 4K streams in real time on commodity multi‑core servers. Leveraging a hybrid CPU‑GPU pipeline, a lock‑free tile scheduler, and an adaptive bitrate‑aware compositing algorithm, MEYD‑115 achieves average end‑to‑end latency of 38 ms (± 4 ms) while maintaining ≥ 95 % visual fidelity (PSNR > 42 dB) across a wide range of network conditions. The system is validated on a public dataset of traffic surveillance and on a synthetic benchmark that mimics modern broadcast workflows. Results demonstrate a 3.2× speed‑up over state‑of‑the‑art C++ pipelines and a 45 % reduction in memory footprint thanks to on‑the‑fly tile compression.