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 adjecent blocks P and Q containing samples p0 and q0 respetively 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 adjecent blocks P and Q containing samples p0 and q0 respetively 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 video decoding method according to the present invention may comprise: a step for determining whether to divide a current block into a plurality of sub-blocks; a step for determining an intra prediction mode for the current block; and a step for performing intra prediction for each sub-block on the basis of the intra prediction mode, when the current block is divided into the plurality of sub-blocks.

Embodiments of the disclosure provide systems and methods for processing video content. The method can include: receiving raw video data of a video; determining a texture complexity for the video based on the raw video data; determining an encoding mode for the raw video data based on the texture complexity; and encoding the raw video data using the determined encoding mode.

Methods and systems for light field image encoding and decoding are disclosed. According to some embodiments, the method receives scene metadata and input light field images associated with a scene. The method further performs a first encoding operation on the scene metadata and the input light field images to generate reference views and reference disparity information. The method further performs a second encoding operation based on the reference views, the reference disparity information, and synthesized residuals to output encoded light field data, where the encoded light field data comprises encoded reference views, encoded reference disparity information, and encoded synthesized residuals. The method further randomly accesses and selects a group of reference views and corresponding disparity information from the encoded light field data based on one or more selected regions of interest. And the method transmits the selected group of reference views, the selected corresponding disparity information, and the encoded synthesized residuals.

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 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 apparatus including a first module and a second module. The first module may be configured to generate one or more values based upon an analysis of one or more samples of a first frame. The second module may be configured to encode one or more samples of a second frame taking into account the one or more values generated by the first module. The one or more values generally represent a measure of an effect on the one or more samples of the first frame of encoding decisions made during encoding of the one or more samples of the second frame.

A method is provided to better detect a scene change to provide a prediction to an encoder to enable more efficient encoding. The method uses a Motion Compensated Temporal Filter (MCTF) that provides motion estimation and is located prior to an encoder. The MCTF provides a Motion Compensated Residual (MCR) used to detect the scene change transition. When a scene is relatively stable, the MCR score is also relatively stable. However, when a scene transition is in process, the MCR score behavior changes, Algorithmically, the MCR score is used by comparing the sliding mean of the MCR score to the sliding median. This comparison highlights the transition points. In the case of a scene cut, the MCR score exhibits a distinct spike. In the case of a fade or dissolve, the MCR score exhibits a transitional period of degradation followed by recovery. By implementing the above detection using the MCR, the location of the I-pictures in the downstream encoding process can be accurately determined for the encoder.

A method is provided to better detect a scene change to provide a prediction to an encoder to enable more efficient encoding. The method uses a Motion Compensated Temporal Filter (MCTF) that provides motion estimation and is located prior to an encoder. The MCTF provides a Motion Compensated Residual (MCR) used to detect the scene change transition. When a scene is relatively stable, the MCR score is also relatively stable. However, when a scene transition is in process, the MCR score behavior changes, Algorithmically, the MCR score is used by comparing the sliding mean of the MCR score to the sliding median. This comparison highlights the transition points. In the case of a scene cut, the MCR score exhibits a distinct spike. In the case of a fade or dissolve, the MCR score exhibits a transitional period of degradation followed by recovery. By implementing the above detection using the MCR, the location of the I-pictures in the downstream encoding process can be accurately determined for the encoder.