The disclosed computer-implemented method may include, for a current frame of a sequence of video frames, determining a frame type label of the current frame. The method may include, in response to determining that the current frame is labeled as an intra frame (I-frame), decoding the current frame and comparing the decoded frame to historical I-frame data. The method may also include, in response to the comparison satisfying a shot-change threshold, flagging the current frame as a shot-change frame, and in response to flagging the current frame as the shot-change frame, storing the current frame for a subsequent shot-change detection. The method may further include updating, based on flagged shot-change frames, shot boundaries for the sequence of video frames. Various other methods, systems, and computer-readable media are also disclosed.

A method for decoding image data can include generating a prediction block based on a prediction mode; generating a quantization block by inversely scanning quantization coefficient information; generating a transform block by inversely quantizing the quantization block using a quantization parameter; generating a residual block by inversely transforming the transform block; generating a reconstructed picture by using the prediction block and the residual block; and applying a deblocking filter on the reconstructed picture, wherein it is determined whether the deblocking filtering is applied between two adjacent blocks P and Q containing samples p0 and q0 respectively by using a boundary quantization parameter when a boundary strength is not zero, the boundary quantization parameter is set to an average value of a quantization parameter of block P and a quantization parameter of block Q, and the quantization parameter is derived by adding a residual quantization parameter and a quantization parameter predictor.

A method for decoding image data can include generating a prediction block based on a prediction mode; generating a quantization block by inversely scanning quantization coefficient information; generating a transform block by inversely quantizing the quantization block using a quantization parameter; generating a residual block by inversely transforming the transform block; generating a reconstructed picture by using the prediction block and the residual block; and applying a deblocking filter on the reconstructed picture, wherein it is determined whether the deblocking filtering is applied between two adjacent blocks P and Q containing samples p0 and q0 respectively by using a boundary quantization parameter when a boundary strength is not zero, the boundary quantization parameter is set to an average value of a quantization parameter of block P and a quantization parameter of block Q, and the quantization parameter is derived by adding a residual quantization parameter and a quantization parameter predictor.

The present disclosure is directed to a method and system for increasing virtual machine (VM) density on a server system through adaptive rendering by dynamically shifting video rendering tasks to a client computing device. In one embodiment, a processor in a server manages virtual machines in the server by controlling a number of VMs and an amount of system resources allocated to the VMs. The number of VMs and the amount of resources allocated to the VMs are controlled by shifting video rendering from at least one of the VMs to a client device, and increasing the number of the VMs in the server after the shifting.

The present disclosure is directed to a method and system for increasing virtual machine (VM) density on a server system through adaptive rendering by dynamically shifting video rendering tasks to a client computing device. In one embodiment, a processor in a server manages virtual machines in the server by controlling a number of VMs and an amount of system resources allocated to the VMs. The number of VMs and the amount of resources allocated to the VMs are controlled by shifting video rendering from at least one of the VMs to a client device, and increasing the number of the VMs in the server after the shifting.

An image decoding method and apparatus according to an embodiment may extract, from a bitstream, a quantization coefficient generated through core transformation, secondary transformation, and quantization; generate an inverse-quantization coefficient by performing inverse quantization on the quantization coefficient; generate a secondary inverse-transformation coefficient by performing secondary inverse-transformation on a low frequency component of the inverse-quantization coefficient, the secondary inverse-transformation corresponding to the secondary transformation; and perform core inverse-transformation on the secondary inverse-transformation coefficient, the core inverse-transformation corresponding to the core transformation.

An image decoding method and apparatus according to an embodiment may extract, from a bitstream, a quantization coefficient generated through core transformation, secondary transformation, and quantization; generate an inverse-quantization coefficient by performing inverse quantization on the quantization coefficient; generate a secondary inverse-transformation coefficient by performing secondary inverse-transformation on a low frequency component of the inverse-quantization coefficient, the secondary inverse-transformation corresponding to the secondary transformation; and perform core inverse-transformation on the secondary inverse-transformation coefficient, the core inverse-transformation corresponding to the core transformation.

An embodiment includes a method and an encoder for SSIM-based bits allocation. The encoder includes a memory and a processor utilized for allocating bits based on SSIM, wherein the processor estimates the model parameter of SSIM-based distortion model for the current picture and determines allocates bits based on the SSIM estimation.

An image processing apparatus includes an image processor and an encoder. The image processor enhances an edge of an input image, removes noise from the input image, synthesizes the edge-enhanced image and the noise-removed image, and removes a high frequency from the synthesized image. The encoder pre-encodes a downsized synthesized image, obtains a pre-bit rate of the pre-encoded image, sets a quantization parameter value based on a reference bit rate and the pre-bit rate, and compresses the high-frequency removed image based on the quantization parameter value.

An image processing apparatus includes an image processor and an encoder. The image processor enhances an edge of an input image, removes noise from the input image, synthesizes the edge-enhanced image and the noise-removed image, and removes a high frequency from the synthesized image. The encoder pre-encodes a downsized synthesized image, obtains a pre-bit rate of the pre-encoded image, sets a quantization parameter value based on a reference bit rate and the pre-bit rate, and compresses the high-frequency removed image based on the quantization parameter value.