as context clustering. 8. Kitten – Optimizes the adaptive quantization for a psychovisual metric. 9. Tortoise – Enables a more thorough adaptive quantization search. You can force-enable several of compression that is applied on this mathematical function is also finetuned by the encoder, this is called Adaptive Quantization. Because the encoder is able to pick the best solution for the compression (Depending weighted. Use all predictors Pixels for MA tree learning. Fraction of pixels used for the Meta-Adaptive Context tree. The MA tree is a way of analyzing the pixels surrounding the current pixel, and depending

as context clustering. 8. Kitten – Optimizes the adaptive quantization for a psychovisual metric. 9. Tortoise – Enables a more thorough adaptive quantization search. You can force-enable several of compression that is applied on this mathematical function is also finetuned by the encoder, this is called Adaptive Quantization. Because the encoder is able to pick the best solution for the compression (Depending weighted. Use all predictors Pixels for MA tree learning. Fraction of pixels used for the Meta-Adaptive Context tree. The MA tree is a way of analyzing the pixels surrounding the current pixel, and depending

as context clustering. 8. Kitten -- Optimizes the adaptive quantization for a psychovisual metric. 9. Tortoise -- Enables a more thorough adaptive quantization search. You can force-enable several compression that is applied on this mathematical function is also finetuned by the encoder, this is called Adaptive Quantization. Because the encoder is able to pick the best solution for the compression (Depending weighted. Use all predictors Pixels for MA tree learning. Fraction of pixels used for the Meta-Adaptive Context tree. The MA tree is a way of analyzing the pixels surrounding the current pixel, and depending

6 个相邻像素的数值进行权衡：上方、左 侧、右上方，加上它们本身的同一方向上的相邻像素。 渐变 + 权衡 – 混合渐变和权衡的方式。 使用所有预测器 MA 树学习像素 用于 Meta-Adaptive Context Tree (元-自适应上下文树) 的像素比例。 MA 树是分析当前像素周围的像素的一种手段，它会根据上下文为这 个像素选择一个预测器。MA 树学习使用的像素越多，它就能更好地 work that the stroke as a whole must perform on an image. A stroke job cannot be canceled while execution and you cannot undo a single job of the stroke without canceling the whole stroke. Example: Lets canvas and commit the undo information. The jobs of the stroke can demand special order of their execution. That is the way how they will be executed on a multi-core machine. Every job can be either of the

对 6 个相邻像素的数值进行权衡：上 方、左侧、右上方，加上它们本身的同一方向上的相邻 像素。 渐变 + 权衡 – 混合渐变和权衡的方式。 使用所有预测器 MA 树学习像素 用于 Meta-Adaptive Context Tree (元-自适应上下文树) 的 像素比例。MA 树是分析当前像素周围的像素的一种手段， 它会根据上下文为这个像素选择一个预测器。MA 树学习使 用的像素越多，它就能更好地理解像素所处环境 work that the stroke as a whole must perform on an image. A stroke job cannot be canceled while execution and you cannot undo a single job of the stroke without canceling the whole stroke. Example: Lets canvas and commit the undo information. The jobs of the stroke can demand special order of their execution. That is the way how they will be executed on a multi-core machine. Every job can be either of the

对 6 个相邻像素的数值进行权衡：上方、左侧、 右上方，加上它们本身的同一方向上的相邻像素。 渐变 + 权衡 – 混合渐变和权衡的方式。 使用所有预测器 MA 树学习像素 用于 Meta-Adaptive Context Tree (元-自适应上下文树) 的像素比例。MA 树是分析当前像素周围的像素的一种手段，它会根据上下文为这个像素 选择一个预测器。MA 树学习使用的像素越多，它就能更好地理解像素 work that the stroke as a whole must perform on an image. A stroke job cannot be canceled while execution and you cannot undo a single job of the stroke without canceling the whole stroke. Example: Lets canvas and commit the undo information. The jobs of the stroke can demand special order of their execution. That is the way how they will be executed on a multi-core machine. Every job can be either of the

对 6 个相邻像素的数值进行权衡：上方、左侧、 右上方，加上它们本身的同一方向上的相邻像素。 渐变 + 权衡 – 混合渐变和权衡的方式。 使用所有预测器 MA 树学习像素 用于 Meta-Adaptive Context Tree (元-自适应上下文树) 的像素比例。MA 树是分析当前像素周围的像素的一种手段，它会根据上下文为这个像素 选择一个预测器。MA 树学习使用的像素越多，它就能更好地理解像素 work that the stroke as a whole must perform on an image. A stroke job cannot be canceled while execution and you cannot undo a single job of the stroke without canceling the whole stroke. Example: Lets canvas and commit the undo information. The jobs of the stroke can demand special order of their execution. That is the way how they will be executed on a multi-core machine. Every job can be either of the

work that the stroke as a whole must perform on an image. A stroke job cannot be canceled while execution and you cannot undo a single job of the stroke without canceling the whole stroke. Example: Lets canvas and commit the undo information. The jobs of the stroke can demand special order of their execution. That is the way how they will be executed on a multi-core machine. Every job can be either of the addition. A barrier job will not start its execution until all the updates (those were requested with setDirty() calls before) has finished their execution. Such behavior is really useful for the case

work that the stroke as a whole must perform on an image. A stroke job cannot be canceled while execution and you cannot undo a single job of the stroke without canceling the whole stroke. Example: Lets canvas and commit the undo information. The jobs of the stroke can demand special order of their execution. That is the way how they will be executed on a multi-core machine. Every job can be either of the significant addition. A barrier job will not start its execution until all the updates (those were requested with setDirty() calls before) has finished their execution. Such behavior is really useful for the case