This disclosure describes systems, methods, and devices related to complexity aware encoding. A device may generate a list of encodes based on pairs of resolution and quantization parameters (QP) pairs associated with one or more video segments received from a source. The device may generate an estimated bit rate associated with the one or more video segments based on an analysis of the one or more video segments. The device may determine distortion values associated with the one or more video segments. The device may apply a weighting mechanism to the distortion values using the estimated bit rate. The device may select a subset of encodes based on the weighting mechanism. The device may perform the subset of encodes on the one or more video segments for transmission.

The embodiments of the present disclosure provide a code rate control method and apparatus, an image acquisition device, and a readable storage medium. The method comprises: acquiring the gain and exposure time of an image to be encoded from an image processing module of an image acquisition device; obtaining corresponding reference distortion degree according to the gain and exposure time of said image; calculating the difference between the distortion degree in a characteristic region of said image and the reference distortion degree; calculating a distortion tolerance degree of macro blocks constituting said image according to the difference between the distortion degree in the characteristic region of said image and the reference distortion degree; performing macro block predictions on the respective macro blocks in said image, to obtain an optimum macro block prediction mode; and encoding said image, which corresponds to the optimum macro block prediction mode, in order to control the code rate of said image. By means of the cooperation of image processing and encoding, said method can achieve code rate control while guaranteeing that the encoded image has a good subjective presentation.

In non-limiting examples of the present disclosure, systems, methods and devices for determining image compression optimums are provided. An image may be processed with a machine learning model that has been trained to identify object types in digital images. A first object and a first object type of the first object may be identified in the image. A first compressed version of the image may be generated, wherein the first compressed version has a first storage size. The first object and the first object type of the first object may be identified in the first compressed version of the image. A second compressed version of the image may be generated based on the identification of the first object and the first object type in the first compressed version of the image. The second compressed version may have a smaller storage size than the first storage size.

In non-limiting examples of the present disclosure, systems, methods and devices for determining image compression optimums are provided. An image may be processed with a machine learning model that has been trained to identify object types in digital images. A first object and a first object type of the first object may be identified in the image. A first compressed version of the image may be generated, wherein the first compressed version has a first storage size. The first object and the first object type of the first object may be identified in the first compressed version of the image. A second compressed version of the image may be generated based on the identification of the first object and the first object type in the first compressed version of the image. The second compressed version may have a smaller storage size than the first storage size.

The present disclosure provides an image encoder. The image encoder is configured to encode an original image and reduce compression loss. The image encoder comprises an image signal processor and a compressor. The image signal processor is configured to receive a first frame image and a second frame image and generates a compressed image of the second frame image using a boundary pixel image of the first frame image. The image signal processor may include memory configured to store first reference pixel data which is the first frame image. The compressor is configured to receive the first reference pixel data from the memory and generate a bitstream obtained by encoding the second frame image based on a difference value between the first reference pixel data and the second frame image. The image signal processor generates a compressed image of the second frame image using the bitstream generated by the compressor.

Provided is an image decoding method including determining a predicted quantization parameter of a current quantization group determined according to at least one of block split information and block size information, determining a difference quantization parameter of the current quantization group, determining a quantization parameter of the current quantization group, based on the predicted quantization parameter and the difference quantization parameter of the current quantization group, and inverse quantizing a current block included in the current quantization group, according to the quantization parameter of the current quantization group.

Techniques are disclosed for the creation of multi-codec encoding profiles (or encoding ladders), which define quality and bitrate for each of the streams made available to clients for streaming a video. In particular, optimization techniques may take into account a quality rate function of each of the codecs when determining the encoding ladder. Additional considerations may include a network bandwidth distribution and/or a distribution of client types.

Techniques are disclosed for the creation of multi-codec encoding profiles (or encoding ladders), which define quality and bitrate for each of the streams made available to clients for streaming a video. In particular, optimization techniques may take into account a quality rate function of each of the codecs when determining the encoding ladder. Additional considerations may include a network bandwidth distribution and/or a distribution of client types.

A two-pass encoding operation is implemented to encode one or more gaming frames into a game stream. The two-pass encoding operation includes a first encoding pass performed on a current frame. As a result of the first encoding pass, an estimated complexity for the current frame is determined. The resulting estimated complexity is then modulated according to a quality difference between reference frames used during the first pass encoding and a subsequent second pass encoding. Based on the modulated complexity, a quantization parameter is determined for the current frame that is then used to perform a second pass encoding on the current frame, resulting in an encoded frame. This encoded frame is then transmitted as part of a stream to a client system.

A two-pass encoding operation is implemented to encode one or more gaming frames into a game stream. The two-pass encoding operation includes a first encoding pass performed on a current frame. As a result of the first encoding pass, an estimated complexity for the current frame is determined. The resulting estimated complexity is then modulated according to a quality difference between reference frames used during the first pass encoding and a subsequent second pass encoding. Based on the modulated complexity, a quantization parameter is determined for the current frame that is then used to perform a second pass encoding on the current frame, resulting in an encoded frame. This encoded frame is then transmitted as part of a stream to a client system.