Camera matrix

The camera matrix, also known as the projection matrix, is a 3×4 matrix in computer vision that maps homogeneous three-dimensional world coordinates to homogeneous two-dimensional image coordinates under the pinhole camera model, encapsulating both intrinsic camera properties and extrinsic pose parameters.^[1]^[2] This linear transformation, denoted as \mathbf{x} = P \mathbf{X}, where \mathbf{X} is a 4×1 world point and \mathbf{x} is a 3×1 image point, enables the projection of 3D scenes onto 2D images while accounting for perspective effects.^[3] The matrix P has 11 degrees of freedom after accounting for scale ambiguity, making it a fundamental tool for tasks like camera calibration and 3D reconstruction.^[2] The camera matrix decomposes into an intrinsic matrix K (3×3) and an extrinsic matrix [R \mid \mathbf{t}] (3×4), such that P = K [R \mid \mathbf{t}].^[1]^[3] The intrinsic matrix K captures internal camera parameters, including focal lengths f_x and f_y (in pixels), the principal point (c_x, c_y) at the image center, and skew coefficient s to model non-orthogonal pixel axes, typically assuming zero skew for simplicity:
K = \begin{bmatrix} f_x & s & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \end{bmatrix}. ^[2]^[3] These five parameters define how 3D rays in the camera coordinate system convert to 2D pixel coordinates.^[2] The extrinsic parameters consist of a rotation matrix R (3×3 orthogonal) and translation vector \mathbf{t} (3×1), which together describe the camera's rigid transformation from world to camera coordinates, with six degrees of freedom (three for rotation and three for position).^[1]^[3] This decomposition allows separate estimation of camera internals from its external pose, often using known 3D-2D correspondences via methods like direct linear transformation (DLT).^[2] In practice, the camera matrix facilitates applications in augmented reality, robotics, and photogrammetry by enabling accurate 3D-to-2D projections and inverse problems like pose estimation.^[3] It assumes an ideal pinhole model, ignoring distortions like radial or tangential effects, which are handled by additional calibration parameters in extended models.^[1]

Pinhole Camera Model

Core Assumptions

The pinhole camera serves as an idealized projection device in computer vision, modeling the formation of images without lens distortion by assuming that all light rays from a scene point converge through a single infinitesimal aperture, or pinhole, before projecting onto a flat image plane behind it.^[2] This geometric abstraction simplifies the complex optics of real cameras, treating the pinhole as the origin of the optical axis where rays intersect without refraction or aberration.^[2] Central to this model are several key assumptions that enable its mathematical tractability: it employs perspective projection, where parallel lines in the 3D world converge to vanishing points in the 2D image; assumes an infinite depth of field, meaning all scene points are equally sharp regardless of distance; excludes radial distortion (such as barrel or pincushion effects) and tangential distortion; and relies on central projection to map 3D world coordinates directly onto 2D image coordinates via straight-line rays through the pinhole.^[2] These idealizations ignore real-world factors like finite aperture size, which would introduce blur, and lens imperfections, focusing instead on pure geometric transformation.^[2] The mathematical foundation of the model is captured by the central projection equation, which relates a 3D world point to its 2D image counterpart through a homogeneous scaling:

s \begin{pmatrix} u \\ v \\ 1 \end{pmatrix} = P \begin{pmatrix} X \\ Y \\ Z \\ 1 \end{pmatrix}

Here, (X, Y, Z) represents the world point in homogeneous coordinates, (u, v) the projected image point, s a non-zero scale factor arising from the homogeneous representation, and P the 3×4 camera matrix encoding the projection.^[2] This equation conceptually introduces the camera matrix without delving into its decomposition, highlighting the linear nature of the projection in projective space. Intrinsic and extrinsic parameters realize these assumptions by separately accounting for the camera's internal geometry and its position relative to the world.^[2] The pinhole model's principles trace their origins to ancient times, with the camera obscura known since the 4th century BC, demonstrating image inversion through a small aperture and laying the groundwork for early photography in the 19th century.^[4] The model was introduced to computer vision in the early 1960s, as in Lawrence Roberts' work on machine perception of three-dimensional solids, with key advancements in calibration techniques in the 1980s through Roger Tsai's methods for accurate parameter estimation via least-squares optimization.^[5]^[6]

Coordinate Systems Involved

The world coordinate system serves as an arbitrary three-dimensional reference frame used to describe the positions of scene points and objects in the physical environment. It is typically defined by an external calibration setup, such as a checkerboard pattern, with its origin and axes chosen for convenience in modeling the scene geometry. This system allows for the representation of 3D points as vectors relative to a global or scene-specific orientation, independent of the camera's position.^[2] In contrast, the camera coordinate system is a local three-dimensional frame centered at the camera's optical center, also known as the pinhole or center of projection. Its origin is at this optical center, with the z-axis aligned along the optical axis pointing toward the scene, the x-axis pointing to the right, and the y-axis pointing downward, forming a right-handed coordinate system parallel to the image plane. This setup positions 3D points relative to the camera's viewpoint, facilitating the projection process by placing the image plane perpendicular to the optical axis at a focal distance along the z-axis.^[2]^[7] The image coordinate system refers to the two-dimensional framework on the camera's sensor or image plane, where points are mapped as pixels. It typically originates at the top-left corner of the digital image grid, with the u-axis extending rightward and the v-axis downward, though the principal point (optical axis intersection) often serves as a reference offset from this corner. Measurements here are in pixel units, converting physical projections into discrete image locations for digital processing.^[2] To handle these transformations uniformly, homogeneous coordinates extend the dimensionalities: 3D points in the world or camera systems become four-dimensional vectors of the form \begin{pmatrix} X & Y & Z & 1 \end{pmatrix}^T, while 2D image points are \begin{pmatrix} u & v & 1 \end{pmatrix}^T. This augmentation introduces a scale factor (the fourth component), enabling projective geometry operations—such as perspective projection—to be expressed as linear matrix multiplications rather than nonlinear divisions, simplifying computations by deferring the perspective divide (u = x/w, v = y/w) until after the matrix application.^[2]^[7] The overall mapping from 3D to 2D relies on a sequential transformation pipeline: first, world coordinates are converted to camera coordinates using extrinsic parameters that account for the camera's position and orientation relative to the world; second, these camera coordinates are projected onto the image coordinate system via intrinsic parameters that model the camera's internal geometry. This pipeline ensures that scene points are accurately rendered in the image plane under the pinhole model's straight-line projections.^[2]^[7]

Intrinsic Parameters

Components of the Intrinsic Matrix

The intrinsic matrix K, a 3×3 upper-triangular matrix, encapsulates the camera's internal geometry by mapping homogeneous 3D coordinates in the camera frame to 2D pixel coordinates on the sensor. It is defined as

K = \begin{pmatrix} f_x & s & u_0 \\ 0 & f_y & v_0 \\ 0 & 0 & 1 \end{pmatrix},

where f_x and f_y denote the effective focal lengths along the horizontal and vertical image axes in pixel units, s represents the skew coefficient, and (u_0, v_0) specifies the principal point coordinates. This formulation assumes a pinhole camera model extended to account for pixel discretization and potential axis misalignment, as detailed in standard geometric models of image formation.^[8] The focal lengths f_x and f_y quantify the scaling factor between the physical distance on the image plane and pixel measurements, derived from the lens's optical focal length divided by the sensor's pixel pitch. Specifically, f_x = f / p_x and f_y = f / p_y, where f is the physical focal length and p_x, p_y are the pixel sizes in each direction; equal values indicate square pixels, while differences reflect the camera's aspect ratio f_x / f_y, which corrects for non-square sensor elements or anamorphic lenses. The principal point (u_0, v_0) indicates the pixel location where the optical axis intersects the image plane, typically near the image center but offset due to mechanical alignment errors in lens mounting or sensor placement. The skew parameter s models the angular deviation between the image axes from perfect orthogonality, often expressed as s = -f_x \cot \theta where \theta is the tilt angle; it is negligible (zero) in most contemporary digital cameras due to precise manufacturing, but non-zero values introduce shearing in the coordinate transformation.^[2]^[8] Normalization via the intrinsic matrix converts observed pixel coordinates (u, v) to normalized coordinates (x, y) on the image plane using K^{-1}, yielding points in metric units relative to the camera center at distance f along the optical axis. The inverse transformation is

\begin{pmatrix} x \\ y \\ 1 \end{pmatrix} = K^{-1} \begin{pmatrix} u \\ v \\ 1 \end{pmatrix},

which undoes the scaling by $1/f_x and $1/f_y, eliminates skew through a shear correction, and translates by the negative principal point offset, producing coordinates where the image plane is at unit distance from the pinhole. This step is essential for downstream tasks like pose estimation, as it standardizes the projection to a canonical form independent of sensor specifics.^[8]^[2] Overall, the intrinsic parameters induce affine transformations—scaling via focal lengths, translation via the principal point, and shearing via skew—on the perspective-projected image, preserving depth-based foreshortening while adapting to the camera's hardware characteristics. These effects ensure accurate mapping from ray directions to discrete pixels without influencing the relative scene geometry determined by external pose.^[8]

Normalized Image Coordinates

In the pinhole camera model, normalized image coordinates refer to a distortion-free representation of points on the image plane at z = 1 in the camera coordinate frame, denoted as (x, y, 1)^T in homogeneous form. These coordinates are related to measured pixel coordinates (u, v, 1)^T through the intrinsic matrix K via the equation \begin{pmatrix} u \\ v \\ 1 \end{pmatrix} = \mathbf{K} \begin{pmatrix} x \\ y \\ 1 \end{pmatrix}, where K encapsulates the camera's internal parameters such as focal lengths and principal point offsets.^[9] This normalization assumes a canonical camera with unit focal length and no skew or offsets, providing a metric basis independent of specific sensor characteristics.^[10] Geometrically, normalized coordinates arise directly from the pinhole projection of a 3D point (X, Y, Z)^T in the camera frame, where x = X/Z and y = Y/Z, projecting the point onto the virtual image plane at Z = 1. This interpretation aligns with an idealized pinhole setup, where rays from the 3D scene pass through the optical center and intersect the plane at these normalized positions, effectively scaling the projection to unit focal length.^[9] Such coordinates preserve the perspective structure of the scene while abstracting away pixel-specific distortions, facilitating analysis in a Euclidean-like space on the image plane.^[11] The inverse mapping from pixel to normalized coordinates is given by \begin{pmatrix} x \\ y \\ 1 \end{pmatrix} = \mathbf{K}^{-1} \begin{pmatrix} u \\ v \\ 1 \end{pmatrix}, which undistorts and rescales observed image points to their canonical form. This step is essential in camera calibration processes, where it enables the minimization of reprojection errors by comparing projected 3D points to observed pixels in a normalized space, improving numerical stability and accuracy.^[12] For instance, in Zhang's calibration method using planar patterns, normalized coordinates help linearize the projection equations for estimating intrinsics.^[12] One key advantage of normalized coordinates is their role in simplifying the overall camera projection matrix to an extrinsic-only form [R | t], as the full matrix P = K [R | t] can be decomposed accordingly, isolating rotation and translation effects. This separation enhances computational efficiency in tasks like pose estimation and structure from motion. Additionally, normalized coordinates enable direct metric interpretations, such as the horizontal field of view (FOV), calculated as 2 \arctan(1/f) in the intrinsic frame but simplifying to 90 degrees for a unit-focal-length canonical camera covering the full normalized extent from -1 to 1.^[9]^[11]

Extrinsic Parameters

Rotation and Translation

The extrinsic parameters of a camera model describe its position and orientation relative to the world coordinate system, enabling the transformation of 3D points from world coordinates to the camera's local coordinate frame. These parameters are encapsulated in the extrinsic matrix, typically represented as [ \mathbf{R} \mid \mathbf{t} ], where \mathbf{R} is a 3×3 rotation matrix and \mathbf{t} is a 3×1 translation vector. This matrix forms the foundational step in the projection pipeline, preceding the application of intrinsic parameters to map points onto the image plane. The rotation matrix \mathbf{R} captures the camera's orientation as a rigid body transformation, satisfying the orthogonality condition \mathbf{R}^\top \mathbf{R} = \mathbf{I} and having determinant \det(\mathbf{R}) = 1 to ensure a proper rotation without reflection. This preserves distances and angles in the transformation from world to camera coordinates. While \mathbf{R} can be parameterized using Euler angles (three sequential rotations around coordinate axes), axis-angle representations (a unit vector and rotation angle), or quaternions (four components with unit norm constraint) for computational efficiency, the matrix form is emphasized in the extrinsic model for direct application in linear algebra operations.^[2] The translation vector \mathbf{t} specifies the positional offset of the camera's origin relative to the world origin, shifting points after rotation. The camera center \mathbf{C}, or optical center, in world coordinates is derived as \mathbf{C} = -\mathbf{R}^{-1} \mathbf{t} (equivalently \mathbf{C} = -\mathbf{R}^\top \mathbf{t} due to orthogonality), representing the point from which all projection rays emanate. The full transformation from a homogeneous world point \mathbf{X}_w = [X_w, Y_w, Z_w, 1]^\top to camera coordinates is given by

\begin{bmatrix} X_c \\ Y_c \\ Z_c \\ 1 \end{bmatrix} = \begin{bmatrix} \mathbf{R} & \mathbf{t} \\ \mathbf{0}^\top & 1 \end{bmatrix} \mathbf{X}_w,

or in non-homogeneous form, \begin{bmatrix} X_c \\ Y_c \\ Z_c \end{bmatrix} = \mathbf{R} (\mathbf{X}_w - \mathbf{C}), where \mathbf{X}_w = [X_w, Y_w, Z_w]^\top. This extrinsic matrix thus rigidly aligns the world frame with the camera frame.^[2] Together, the extrinsic parameters provide 6 degrees of freedom: 3 for rotation (spanning the special orthogonal group SO(3)) and 3 for translation (spanning \mathbb{R}^3), allowing full specification of the camera's 3D pose in the world without internal distortions. These degrees of freedom are essential for calibration and pose estimation in computer vision applications, such as structure-from-motion and augmented reality.^[1]

Camera Pose from Extrinsics

The camera pose encapsulates the rigid body transformation that positions and orients the camera within the world coordinate system, comprising the camera's location \mathbf{C} and its orientation given by the rotation matrix \mathbf{R}. In standard computer vision conventions of the pinhole camera model, the camera frame is defined such that the optical axis aligns with the positive z-axis, pointing towards the scene; some graphics conventions use the negative z-axis. The extrinsic parameters \mathbf{R} and translation vector \mathbf{t} relate world points \mathbf{X}_w to camera coordinates via \mathbf{X}_c = \mathbf{R} (\mathbf{X}_w - \mathbf{C}), where \mathbf{t} = -\mathbf{R} \mathbf{C}.^[13]^[9] From the extrinsic parameters, the optical center \mathbf{C} is computed as \mathbf{C} = -\mathbf{R}^T \mathbf{t}, leveraging the orthogonality of \mathbf{R} where \mathbf{R}^{-1} = \mathbf{R}^T. The complete pose can then be assembled into the camera-to-world transformation matrix [\mathbf{R}^T \mid -\mathbf{R}^T \mathbf{C}], which inverts the world-to-camera mapping [\mathbf{R} \mid \mathbf{t}]. This formulation allows direct recovery of the camera's 6 degrees of freedom (3 translational, 3 rotational) from the extrinsics alone.^[9] Camera orientation is commonly parameterized using roll-pitch-yaw (RPY) Euler angles, which apply successive rotations about the x-axis (roll), y-axis (pitch), and z-axis (yaw) in a fixed sequence, such as ZYX convention prevalent in computer vision applications. However, RPY representations are prone to gimbal lock, a singularity where the pitch angle reaches \pm 90^\circ, causing the roll and yaw axes to align and eliminating one degree of freedom, which can lead to unstable or ambiguous orientations in pose estimation.^[9]^[14] The camera pose is typically estimated from a set of known 3D world points and their corresponding 2D image projections using Perspective-n-Point (PnP) algorithms, which solve for \mathbf{R} and \mathbf{t} given the calibrated intrinsics. The minimal configuration requires at least 3 non-collinear points for a solution, though more points enhance robustness against noise; the Direct Linear Transformation (DLT) method linearizes the problem by constructing a homogeneous system from the correspondences and solving via singular value decomposition to recover the projection matrix, from which extrinsics are decomposed. For efficiency with larger point sets, the EPnP algorithm provides an accurate linear-time solution by reducing the pose to a linear subsystem over the 4 virtual control points of the camera's homography.^[9]^[15] PnP solutions often yield multiple candidates due to inherent ambiguities in the perspective projection; for the minimal P3P case, up to 4 possible poses exist, which are disambiguated by enforcing chirality constraints to ensure all reconstructed points lie in front of the camera (positive depth in camera coordinates).^[9]

Camera Matrix Construction

Composition into Full Matrix

The full camera matrix P, a 3×4 projection matrix, is formed by combining the 3×3 intrinsic matrix K with the 3×4 extrinsic matrix [R \mid t], where R is the 3×3 rotation matrix and t is the 3×1 translation vector; specifically, the first three columns of P are given by K R and the fourth column by K t.^[16] This composition allows for the direct projection of a 3D world point \mathbf{X} = \begin{bmatrix} X & Y & Z & 1 \end{bmatrix}^T onto the 2D image plane via the homogeneous equation

s \begin{bmatrix} u \\ v \\ 1 \end{bmatrix} = P \mathbf{X},

where s is the depth-dependent scale factor, computed as s = \mathbf{p}_3 \cdot \mathbf{X} and \mathbf{p}_3 is the third row of P.^[9] The resulting inhomogeneous pixel coordinates (u, v) are obtained by dehomogenization:

u = \frac{\mathbf{p}_1 \cdot \mathbf{X}}{\mathbf{p}_3 \cdot \mathbf{X}}, \quad v = \frac{\mathbf{p}_2 \cdot \mathbf{X}}{\mathbf{p}_3 \cdot \mathbf{X}},

with \mathbf{p}_1 and \mathbf{p}_2 denoting the first and second rows of P, respectively.^[9] As a projective transformation, the matrix P has rank 3 and a one-dimensional null space spanned by the homogeneous coordinates of the camera center. It is defined up to an arbitrary non-zero scale factor, yielding 11 degrees of freedom in total: 5 from the intrinsic parameters and 6 from the extrinsic parameters.^[9]^[17]

Derivation of Projection

The derivation of the camera projection matrix begins with the pinhole camera model, which maps a 3D point in the world coordinate system to a 2D point in the image plane through a series of geometric transformations. Consider a 3D point \mathbf{X}_w = (X_w, Y_w, Z_w)^T in world coordinates. To project this point onto the image, it is first transformed into the camera coordinate system using the extrinsic parameters: a rotation matrix R (a 3×3 orthogonal matrix) and a translation vector \mathbf{t} (a 3×1 vector). The camera coordinates are given by \mathbf{X}_c = R \mathbf{X}_w + \mathbf{t}, where \mathbf{t} = -R \mathbf{C} and \mathbf{C} is the camera center in world coordinates.^[9] In homogeneous coordinates, this transformation is represented compactly as a 3×4 extrinsic matrix [R \mid \mathbf{t}] applied to the augmented point \tilde{\mathbf{X}}_w = (X_w, Y_w, Z_w, 1)^T, yielding \mathbf{X}_c = (X_c, Y_c, Z_c)^T = [R \mid \mathbf{t}] \tilde{\mathbf{X}}_w. This avoids explicit division at this stage and maintains linearity. The perspective projection then occurs along the optical axis (Z-axis in camera coordinates), where the image plane is assumed to be at Z = 1 in normalized units.^[9] The perspective division produces normalized image coordinates (x, y) by scaling with the depth Z_c:

\begin{pmatrix} x \\ y \\ 1 \end{pmatrix} = \begin{pmatrix} X_c / Z_c \\ Y_c / Z_c \\ 1 \end{pmatrix}.

These coordinates lie on the normalized image plane and represent the direction from the camera center to the world point, independent of distance. To map to pixel coordinates in the sensor array, the intrinsic matrix K is applied, which accounts for focal length, principal point, and pixel scaling:

\begin{pmatrix} u \\ v \\ 1 \end{pmatrix} = K \begin{pmatrix} x \\ y \\ 1 \end{pmatrix},

where K is the 3×3 upper-triangular calibration matrix.^[9] Combining these steps in homogeneous coordinates yields the full projection equation. Substituting the perspective and intrinsic transformations into the extrinsic mapping gives:

s \begin{pmatrix} u \\ v \\ 1 \end{pmatrix} = K [R \mid \mathbf{t}] \begin{pmatrix} X_w \\ Y_w \\ Z_w \\ 1 \end{pmatrix},

where s = Z_c is an arbitrary non-zero scale factor ensuring the third component is 1 after normalization. Thus, the camera projection matrix P = K [R \mid \mathbf{t}] is a 3×4 matrix that directly maps homogeneous world points to homogeneous image points, encapsulating the entire pinhole projection geometry. The dehomogenized pixel coordinates are then (u/s, v/s). This form has 11 degrees of freedom: 5 from K and 6 from the rigid-body motion defined by R and \mathbf{t}.^[9]

Matrix Properties and Analysis

Normalized Camera Matrix

The normalized camera matrix P_n represents a specialized form of the camera projection matrix where the intrinsic parameters are assumed to be identity, i.e., K = I. This results in P_n = [R \mid t], a 3×4 matrix composed of a 3×3 rotation matrix R and a 3×1 translation vector t, which directly maps 3D world points to normalized image coordinates.^[16] In this setup, the projection occurs onto a unit-focal plane with the principal point at the origin, yielding coordinates x and y in metric units.^[9] The projection equation for the normalized camera matrix is given by

s \begin{pmatrix} x \\ y \\ 1 \end{pmatrix} = [R \mid t] \begin{pmatrix} X \\ Y \\ Z \\ 1 \end{pmatrix},

where (X, Y, Z) are the 3D world coordinates, (x, y) are the normalized 2D image coordinates, and s is a non-zero scale factor ensuring the third component is 1.^[16] This form simplifies the perspective projection to pure extrinsic geometry, eliminating the effects of focal length and principal point offset.^[9] A key advantage of the normalized camera matrix is the reduction of parameters from the general 11 degrees of freedom in P to 6 degrees of freedom, corresponding solely to the extrinsic parameters (3 for rotation and 3 for translation).^[16] This simplification facilitates calibration processes by focusing computations on pose estimation and aligns directly with fundamental perspective geometry principles, enhancing numerical stability in algorithms like the Direct Linear Transformation (DLT).^[9] The normalized form relates to the general camera matrix P through P_n = K^{-1} P, allowing any projection matrix to be normalized for analysis by inverting the known intrinsics.^[16] However, this assumes perfect rectification with no distortions or non-unit intrinsics, which limits its direct applicability to real cameras that require denormalization via multiplication by K to obtain pixel coordinates.^[9]

Decomposition and Camera Position

The camera matrix P can be decomposed into its intrinsic matrix K and extrinsic parameters [R \mid t], where P = K [R \mid t], with K being upper triangular and R a 3×3 orthogonal rotation matrix. This factorization is achieved by applying RQ decomposition to the first three columns of P, denoted as M = P[:, 1:3], yielding M = K R, where the RQ algorithm ensures K has positive diagonal elements corresponding to the camera's focal lengths and principal point. The translation vector t is then recovered as t = K^{-1} P[:, 4]. This method, detailed in seminal computer vision literature, provides a direct way to separate internal camera parameters from external pose, assuming a full-rank P.^[18] Recovering the camera position, or center C, from P involves finding the right null space of the matrix, as P C = 0 in homogeneous coordinates, since the camera center projects to undefined points in the image. This null space is one-dimensional for a rank-3 P, and solving the linear system P \tilde{C} = 0 (where \tilde{C} is homogeneous) yields C up to scale; dehomogenization provides the 3D position. Alternatively, after decomposition, C = -R^T t, which aligns with the extrinsic formulation where the camera pose transforms world points to camera coordinates. Both approaches confirm the camera's location in world space without requiring additional data.^[18] The rotation matrix R from RQ decomposition may occasionally result in an improper orthogonal matrix (determinant -1), necessitating refinement via polar decomposition to extract a proper rotation: R = U V^T, where M = U \Sigma V^T is the SVD of M, ensuring R represents a valid orientation. For initial estimation of P itself from image-world point correspondences, the Direct Linear Transformation (DLT) algorithm solves a linear system A p = 0 (where p = \mathrm{vec}(P)) using at least six points, minimizing algebraic error via SVD on the constraint matrix A. This yields P up to an arbitrary scale, as projective transformations are defined projectively. Subsequent nonlinear refinement, such as Levenberg-Marquardt optimization, minimizes geometric reprojection error to separate and optimize intrinsics and extrinsics, incorporating constraints like zero skew (K_{1,2} = 0) for uniqueness. Without such constraints, decomposition suffers from ambiguities, as P scales do not affect projections, and skew or aspect ratio may trade off.^[18]^[19]