Abstract: Inter-color image prediction is based on color grading modeling. Prediction is applied to the efficient coding of images and video signals of high dynamic range. Prediction models may include a color transformation matrix that models hue and saturation color changes and a non-linear function modeling color correction changes. Under the assumption that the color grading process uses a slope, offset, and power (SOP) operations, an example non linear prediction model is presented.

Abstract: A first video signal is accessed, and represented in a first color space with a first color gamut, related to a first dynamic range. A second video signal is accessed, and represented in a second color space of a second color gamut, related to a second dynamic range. The first accessed video signal is converted to a video signal represented in the second color space. At least two color-related components of the converted video signal are mapped over the second dynamic range. The mapped video signal and the second accessed video signal are processed. Based on the processing, a difference is measured between the processed first and second video signals. A visual quality characteristic relates to a magnitude of the measured difference between the processed first and second video signals. The visual quality characteristic is assessed based, at least in part, on the measurement of the difference.

Abstract: Enhancing image dynamic range is described. An input video signal that is represented in a first color space with a first color gamut, which is related to a first dynamic range, is converted to a video signal that is represented in a second color space with a second color gamut. The second color space is associated with a second dynamic range. At least two (e.g., three) color-related components of the converted video signal are mapped over the second dynamic range.

Abstract: Optimal error metric function for motion estimation is determined and used for video coding and/or video processing of images. To do so, an initial motion estimation using an initial error metric function can be performed. This can produce motion prediction errors. If the initial error metric function is not the optimal error function, then a final motion estimation is performed using a selected optimal error metric function. In some embodiments, a shape of error distribution can be used to determine the optimal error metric function. Some example systems or devices for this motion estimation can include systems or devices for compression, temporal interpolation, and/or super-resolution processing.

Abstract: A first image stream has a first dynamic range and a first color space. First and the second image streams are received in a layered codec. The second image stream has a second dynamic range, which is higher than the first dynamic range. The first image stream is in the codec's base layer; the second image stream is in its enhancement layer. The first image stream is encoded to obtain an encoded image stream, which is decoded to obtain a decoded image stream. The decoded image stream is converted from the first non-linear or linear color space to a second, different color space to obtain a color converted image stream. A higher dynamic range image representation of the color converted image stream is generated to obtain a transformed image stream. Inverse tone mapping parameters are generated based on the transformed image stream and the second image stream.

Abstract: Optimal error metric function for motion estimation is determined and used for video coding and/or video processing of images. To do so, an initial motion estimation using an initial error metric function can be performed. This can produce motion prediction errors. If the initial error metric function is not the optimal error function, then a final motion estimation is performed using a selected optimal error metric function. In some embodiments, a shape of error distribution can be used to determine the optimal error metric function. Some example systems or devices for this motion estimation can include systems or devices for compression, temporal interpolation, and/or super-resolution processing.

Abstract: Unbiased rounding of unsigned data is employed in the decoding or the encoding and decoding of digital bitstreams representing data-video when the video is encoded at a first bit depth and is decoded at a second bit depth, lower than the first bit depth. The unbiased rounding may be employed in processing that employs a prediction loop. When the data-compressed video is represented in frames, the unbiased rounding may be of inter-frame and/or intra-frame data.

Abstract: The quantization parameter QP is well-known in digital video compression as an indication of picture quality. Digital symbols representing a moving image are quantized with a quantizing step that is a function QSN of the quantization parameter QP, which function QSN has been normalized to the most significant bit of the bit depth of the digital symbols. As a result, the effect of a given QP is essentially independent of bit depth—a particular QP value has a standard effect on image quality, regardless of bit depth. The invention is useful, for example, in encoding and decoding at different bit depths, to generate compatible, bitstreams having different bit depths, and to allow different bit depths for different components of a video signal by compressing each with the same fidelity (i.e., the same QP).