High-capacity MIMO visible light communication integrated into mini-LED LCDs.

Visible light communication (VLC) can be integrated into a liquid crystal display (LCD) by modulating its backlight while normally showing pictures. Received by ordinary cameras, such integrated display and communication (IDAC) systems are promising for the Internet of Things and Metaverse. However, in the premise of unaffected display function, the capacity of current IDAC systems is limited, with data rates of very few kbps. This work proposes a new architecture: multiple-input, multiple-output (MIMO) VLC integrated into a mini-LED LCD, whose many backlight segments act as multiple transmitters. A camera utilizes the rolling shutter effect with independent pixel columns to form multiple outputs. The communication capacity is thus significantly multiplied by the backlight column number. In addition, local dimming, which is favorable for an LCD's contrast and power consumption, is exploited to achieve efficient signal modulation. We built a mini-LED LCD prototype with 8-by-20 backlight segments for experimental verification. The backlight segments multiplex a video-rate signal for local dimming and a high-frequency (∼34 kHz) signal modulated through multi-pulse position modulation (MPPM) for VLC. By taking photographs with a camera 1.1 m away from the screen, a record-high rate of 201.6 kbps (approximately ten times faster than current IDAC systems) was experimentally achieved with a bit error rate satisfying the forward error correction. Improved image contrast due to local dimming was also observed.

Enhancing the spatial resolution of light-field displays without losing angular resolution by a computational subpixel realignment.

Low spatial resolution is an urgent problem in integral imaging light-field displays (LFDs). This study proposes a computational method to enhance the spatial resolution without losing angular resolution. How rays reconstruct voxels through lenslets is changed so that every ray through a lenslet merely provides a subpixel. The three subpixels of a pixel no longer form one voxel but three independent voxels. We further demonstrate imperfect integration of subpixels, called the sampling error, can be eliminated on specific image depths, including the central depth plane. By realigning subpixels in the above manner under no sampling error, the sampling rate of voxels is three times the conventional pixel-based LFDs. Moreover, the ray number of every voxel is preserved for an unaffected angular resolution. With unavoidable component alignment errors, resolution gains of 2.52 and 2.0 are verified in simulation and experiment by computationally updating the elemental image array. The proposed computational method further reveals that LFDs intrinsically have a higher space-bandwidth product than presumed.

Deep learning-based real-time driving for 3-field sequential color displays with low color breakup and high fidelity.

Field sequential color liquid crystal displays (FSC-LCDs) are promising for applications needing high brightness and high resolution because removing color filters brings three times the light efficiency and spatial resolution. In particular, the emerging mini-LED backlight introduces compact volume and high contrast. However, the color breakup severely deteriorates FSC-LCDs. Concerning color breakup, various 4-field driving algorithms have been proposed at the cost of an additional field. In contrast, although 3-field driving is more desired due to fewer fields used, few 3-field methods that can balance image fidelity and color breakup for diverse image content have been proposed. To develop the desired 3-field algorithm, we first derive the backlight signal of one multi-color field using multi-objective optimization (MOO), which achieves a Pareto optimality between color breakup and distortion. Next, considering the slow MOO, the MOO-generated backlight data forms a training set to train a lightweight backlight generation neural network (LBGNN), which can produce a Pareto optimal backlight in real-time (2.3 ms on GeForce RTX 3060). As a result, objective evaluation demonstrates a reduction of 21% in color breakup compared with currently the best algorithm in color breakup suppression. Meantime, the proposed algorithm controls the distortion within the just noticeable difference (JND), successfully addressing the conventional dilemma between color breakup and distortion for 3-field driving. Finally, experiments with subjective evaluation further validate the proposed method by matching the objective evaluation.

Two-field sequential color liquid crystal displays with deep learning-enabled real-time driving.

Two-field driving is the ultimate goal of field sequential color liquid crystal displays (FSC-LCDs) because it requires the lowest refresh rate and transmission bandwidth in addition to the intrinsic advantages of FSC-LCDs, e.g., tripled light efficiency and spatial resolution. However, fewer fields create a more significant challenge in controlling color breakup and distortion, as well as higher computational complexity in calculating LC signals. Regarding the difficulties, we propose a two-field FSC driving method that synchronously generates backlight and LC signals through two lightweight neural networks. The runtimes of the two networks are as fast as 1.23 and 1.79 ms per frame on a GeForce RTX 3090Ti graphic card, fully supporting real-time driving. Next, an over-partitioning approach is proposed to overcome the cross talk between backlight segments while processing high-resolution images. Besides the real-time feature, a reduction of 14.88% in color breakup concerning current methods and low distortion are verified. We also provide our open-source code.

Deep learning-enabled image content-adaptive field sequential color LCDs with mini-LED backlight.

The mini-LED as the backlight of field sequential color LCD (FSC-LCD) enables high contrast, thin volume, and theoretically tripled light efficiency and resolution. However, color breakup (CBU) induced by a relative speed between an observer and the display severely limits the application of FSC-LCDs. Several driving algorithms have been proposed for CBU suppression, but their performance depends on image content. Moreover, their performance plateaus with increasing image segment number, preventing taking advantage of the massive segments introduced by mini-LEDs. Therefore, this study proposes an image content-adaptive driving algorithm for mini-LED FSC-LCDs. Deep learning-based image classification accurately determines the best FSC algorithm with the lowest CBU. In addition, the algorithm is heterogeneous that the image classification is independently performed in each segment, guaranteeing minimized CBU in all segments. We perform objective and subjective validation. Compared with the currently best algorithm, the proposed algorithm improves the performance in suppressing CBU by more than 20% using two evaluation metrics, supported by experiment-based subjective evaluation. Mini-LED FSC-LCDs driven by the proposed algorithm with outstanding CBU suppression can be ideal for display systems requiring high brightness and high resolution, such as head-up displays, virtual reality, and augmented reality displays.