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High resolution 8k UHD displays for emerging technologies like connected cars, IoT, and AR/VR (Augmented/Virtual Reality) require high bandwidth to support the high-resolution transmission. MIPI DSI is the widely used display interface, but the bandwidth provided by PHY layers isn’t sufficient enough to support the high-resolution displays; therefore, a compression technique like DSC (Display Stream Compression) is required. One of our recent blog discussed about DSC 1.2 in HDMI 2.1 – High Resolution Displays for Mobile, TV, PC and Automotive Enabled by DSC 1.2 in HDMI 2.1. In this blog, we will see how DSC 1.2 enables MIPI DSI to support the high-resolution displays for emerging applications.
If we analyze the gap between display resolution and PHY bandwidth over the past few years, we realize that display resolution is increasing rapidly; however, the bandwidth of PHY layers has not observed as much growth. The gap is depicted in the chart below:
To bridge this gap, users can either increase the number of parallel PHYs or use an efficient compression technique, such as DSC, to keep up with the demand of information in high display resolutions without increasing circuit complexity.
The DSC v1.2, developed by Video Electronics Standard Association (VESA), provides a wide variety of features to scale down the required bandwidth by 2 to 3 times.
Wide Range of Applications:
Some of the high resolutions are impossible to be implemented without 2x to 3x compression capabilities of DSC v1.2. DSC also considerably reduces the cost, area, and power simultaneously, and has become a mandatory industry norm nowadays.
草榴社区 VIP for MIPI DSI supports the latest VESA DSC v1.2 compression scheme. The VESA DSC 1.2 module is fully integrated in VIP for MIPI DSI, providing a complete solution for verification of high-resolution displays using compressed video across a wide spectrum of applications. Watch this space for further developments in the world of display and other protocols.
To understand DSC algorithm in more detail, refer our white paper – Analyzing the Losses in Visually Lossless Compression Algorithms.