It seem that some thread has occupancy the ipu task 1. I have patch the file into kernel follow the instruction in I.MX6Q + ADV7180. The de-interlace is set, but the quality is not good. This code is not an official delivery and as such no guarantee of support for this code is provided by Freescale.ĬVBS PAL/NTSC video is input to Video decoder ADV7180, and ADV7180 connect to I.MX6Q CSI BT656. The driver was tested on a Sabre AI Rev A board running Linux 12.02. The requested format and size should match what can be provided directly by the sensor. This driver does not support resize or color space conversion on the way to memory. An example sequence for running the test is as follows: Attached is also a modified the tvin unit test to give an example of how to use the new driver. The selection is done by passing the index “2” as an argument to the VIDIOC_S_INPUT V4L2 ioctl. This patch adds another input device to the V4L2 framework that can be selected to perform the deinterlacing on the way to memory. The patch might need to be adapted to newer BSPs, However, the logic and functionality is there and should shorten the development time. Below is a patch created against a rather old BSP version that adds support for de-interlaced V4L2 capture. In the BSP, HW accelerated de-interlacing is only supported in the V4L2 output stream. In this test, Pixop's Deinterlacer is clearly the most accurate performer of the three, both in terms of PSNR and SSIM (higher numbers are better).In some cases it is desired to directly have progressive content available from a TV-IN interface through the V4L2 capture device. Pixop Deinterlacer: production model available in our web app output encoded via H.264 37.2 Mbpsįor each deinterlacer, its performance was evaluated in relation to the ground truth based on both the standard PSNR and SSIM metrics on the luminance 8-bit channel:.YADIF, Bob Weaver and Weston Three-Field: video filters built into FFmpeg 4.3-2 using default parameters output encoded via lossless FFV1.We then ran four deinterlacers on the interlaced version: From the ground truth SD, we then created an interlaced version using FFmpeg with parameters "tinterlace=interleave_top", which creates a top-fields first interlaced version. Initially, the source video was downscaled and cropped from 1080p HD to 720x576 pixels via FFmpeg in order to produce a ground truth baseline. We conducted a test in November 2020, of Pixop Deinterlacer's performance relative to a couple of other algorithms on the 15-second pedestrian_area sequence which is part of Derf's Test Media Collection at. This type of multi-frame approach is common among deinterlacing algorithms as it allows better filtering to be achieved for regions in a frame with little or no motion. An enhanced block of four deinterlaced frames is produced via inference using our pre-trained neural network model as shown in the diagram below: Video is processed in blocks of two interlaced video frames as input. We performed extensive validation on the trained model using several different video sources to ensure that the output is consistently attractive to the end-user. These degradations have been carefully engineered to resemble the type of deinterlacing artifacts commonly found in video. Our deep convolutional neural network (CNN) architecture uses a combination of spatial and temporal filtering, learning how to spatially deinterlace frames and then optimally combine the effects of motion and temporal imperfections to generate the deinterlaced output.ĭuring the learning phase, the CNN is presented with tens of thousands of image pairs of artificially degraded and perfect image patches. In a nutshell, this AI filter can reduce: This solution significantly reduces various aliasing artifacts that appear when fine details are deinterlaced. Pixop Deinterlacer is our filter for enhancing the perceived visual quality of deinterlaced video compared to classic algorithms.
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