1. Field
Embodiments of the present invention relate to display devices.
2. Related Art
A variety of display devices have been developed. Examples thereof include liquid crystal display devices, field emission display devices, plasma display devices, and organic light emitting display devices. These displays are lighter in weight and smaller in volume than conventional cathode ray tube displays.
For historic and perceptual reasons, input image data/data of a digital video signal, which corresponds to images to be displayed on a display panel of a display device, is often encoded with a highly nonlinear function, which may be referred to as an Electro-Optical Transfer Function (EOTF). The EOTF effectively describes how to turn a digital code word corresponding to input image data into visible information to be perceived by a user. Some EOTFs include gamma nonlinearity, which was commonly used for cathode ray tube (CRT) display devices, Perceptual Quantizer (PQ) (e.g., SMPTE S-2084), DICOM (Digital Imaging and Communications), etc. A shape of an effective EOTF will correspond to qualities of the visual perception of the images by the user. Furthermore, by the conclusion of all data processing, the EOTF of the encoded data should be consistent with the EOTF of the display device such the images are presented correctly.
There may be a number of reasons why display devices employ nonlinear encoding strategies. One reason is that it is desirable to assign the input image data to perceptually relevant gray scale levels. Such nonlinearity may be understood when it is realized that the user perception of display characteristics may also be nonlinear. For example, the user may be able to perceive small changes in brightness, or small changes in light intensity (e.g., one nit, or one candela per meter squared, of difference) at a dark end of the spectrum of gray scale levels, while the user may not be able to perceive differences in brightness that are less than a change of about 10 nits at the bright end of the spectrum of gray scale levels (e.g., at an end of the spectrum corresponding to about 100 nits or more). That is, differences between adjacent gray scale levels are more easily observed at lower gray scale levels at a lower end of the spectrum.
For example, in an EOTF of a conventional display device, differences between adjacent digital values at the low/left end of a graph representing the EOTF may correspond to a fairly small change in brightness, while differences between adjacent digital values at the far right end of the graph correspond a fairly large change in brightness. However, the differently sized changes in brightness may have roughly the same level of detectability by the user.
Accordingly, such nonlinear sensitivity of the human eye to changes in brightness may be suitably reflected in the EOTF, which may correspond to higher precision (e.g., lower compression) in dark regions at the left end of the graph, while allowing for lower precision (e.g., higher compression) in bright regions at the right end of the graph.
Oftentimes the calibration process may involve adjusting the shape of the EOTF and image quality may correspond to the shape and smoothness of the EOTF. If the shape of the EOTF curve is poorly approximated, quantization blockiness and other undesirable artifacts may be perceived by the user in images displayed by the display device. Accordingly, a well-defined EOTF lacking “bumps” or “kinks” may be suitable for quality display of images.
Furthermore, it can be challenging to mathematically compute the EOTF using a hardware-based integrated circuit. That is, although the analytical shape of the EOTF in hardware may be fairly simple, the low-cost, analytical calculation of the EOTF in an embedded circuit may be difficult. One solution is to store pre-computed functions as a look up table (LUT) in memory. Alternatively, the EOTF can be stored in an inverse LUT in logic.
Both approaches may be non-ideal for high dynamic range displays. The inverse LUT uses logic to store a plurality of tables needed to calculate the nonlinear value. Use of memory is impractical when many LUTs are needed. Storing the LUTs in logic does not permit them to be adjusted. The conventional LUT has been widely used for SDR imagery, but it does not scale well for high dynamic imagery. The size of the LUT can become too large to be practical.
Accordingly, modern video-processing systems deal with these types of nonlinear functions (e.g., EOTFs). Such video-processing systems receive nonlinearly encoded data representing the input image data, but may seek to perform various calculations or adjustments of the input image data (e.g., to adjust color, to sharpen contrast, to adjust brightness, etc.). Because it may be suitable to deal with such adjustments in the linear domain/linear space, as opposed to the nonlinear domain/nonlinear space in which the input image data lies, the system may seek to linearize the received nonlinear input image data so that the system may more easily perform various mathematical calculations on the data.
For example, in reproducing certain colors, such as a greenish-yellow, the ratio of red pixel values to green pixel values, which is used to reproduce such a color, may be a relevant factor in color reproduction. However, that particular ratio of pixel values may change dramatically depending on whether the image representation occurs in the linear domain or in the nonlinear domain. Because the linear domain corresponds to the user's perception, most changes or scaling of the ratio of pixel values, or color corrections, is done in the linear domain in a manner similar to the brightness adjustment described above (e.g., where smaller changes in relevant ratios of corresponding pixel values result in a more easily perceived change in color for certain shades and hues than others). However, such linear processing corresponds to higher precision (e.g., larger bit depth) than the nonlinear representation to maintain display of a quality image.
After linear processing is complete, the signal may be re-encoded into the nonlinear domain. In a display system, such nonlinear encoding is often referred to as gamma correction. However, nonlinear encoding may be difficult to perform with hardware-based processing, as high dynamic range (HDR) imaging makes nonlinear encoding more difficult.
The above information disclosed in this Background section is only to enhance the understanding of the background of the invention, and therefore it may contain information that does not constitute prior art.